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A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data (2403.05606v1)
Published 8 Mar 2024 in cs.LG, cs.AI, cs.CL, and cs.CV
Abstract: Diagnosing rare diseases presents a common challenge in clinical practice, necessitating the expertise of specialists for accurate identification. The advent of machine learning offers a promising solution, while the development of such technologies is hindered by the scarcity of data on rare conditions and the demand for models that are both interpretable and trustworthy in a clinical context. Interpretable AI, with its capacity for human-readable outputs, can facilitate validation by clinicians and contribute to medical education. In the current work, we focus on choroid neoplasias, the most prevalent form of eye cancer in adults, albeit rare with 5.1 per million. We built the so-far largest dataset consisting of 750 patients, incorporating three distinct imaging modalities collected from 2004 to 2022. Our work introduces a concept-based interpretable model that distinguishes between three types of choroidal tumors, integrating insights from domain experts via radiological reports. Remarkably, this model not only achieves an F1 score of 0.91, rivaling that of black-box models, but also boosts the diagnostic accuracy of junior doctors by 42%. This study highlights the significant potential of interpretable machine learning in improving the diagnosis of rare diseases, laying a groundwork for future breakthroughs in medical AI that could tackle a wider array of complex health scenarios.
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[14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. 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[33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Johnson, A. E. et al. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Scientific data 6, 317 (2019). [3] Lin, M. et al. Improving model fairness in image-based computer-aided diagnosis. Nature Communications 14, 6261 (2023). [4] Gao, M. et al. Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis. Medical Image Analysis 89, 102884 (2023). [5] Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34, 1993–2024 (2014). [6] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [7] Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A. & Sangiorgi, L. Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Lin, M. et al. Improving model fairness in image-based computer-aided diagnosis. Nature Communications 14, 6261 (2023). [4] Gao, M. et al. Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis. Medical Image Analysis 89, 102884 (2023). [5] Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34, 1993–2024 (2014). [6] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [7] Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A. & Sangiorgi, L. Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Gao, M. et al. Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis. Medical Image Analysis 89, 102884 (2023). [5] Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34, 1993–2024 (2014). [6] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [7] Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A. & Sangiorgi, L. Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34, 1993–2024 (2014). [6] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [7] Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A. & Sangiorgi, L. Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. 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A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A. & Sangiorgi, L. Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. 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Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. 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[32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. 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Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Varoquaux, G. & Cheplygina, V. 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[22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. 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New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). 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Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. 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M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Lin, M. et al. Improving model fairness in image-based computer-aided diagnosis. Nature Communications 14, 6261 (2023). [4] Gao, M. et al. Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis. Medical Image Analysis 89, 102884 (2023). [5] Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34, 1993–2024 (2014). [6] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [7] Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A. & Sangiorgi, L. Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Gao, M. et al. Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis. Medical Image Analysis 89, 102884 (2023). [5] Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34, 1993–2024 (2014). [6] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [7] Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A. & Sangiorgi, L. Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34, 1993–2024 (2014). [6] Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [7] Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A. & Sangiorgi, L. Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Liew, S.-L. et al. A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data 5, 1–11 (2018). [7] Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A. & Sangiorgi, L. Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A. & Sangiorgi, L. Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. 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Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. 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Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. 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Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. 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[16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. 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Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. 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Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). 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Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. 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Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. 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International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). 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[10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. 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K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. 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[32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. 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IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. 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Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. 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Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. 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Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. 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IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A. & Sangiorgi, L. Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. 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G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). 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Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. 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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. 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K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. 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IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. 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Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. 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Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. 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Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. 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Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. 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Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A. & Sangiorgi, L. Opportunities and challenges for machine learning in rare diseases. Front Med (Lausanne) 8, 747612 (2021). [8] Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Molnar, M. J. & Molnar, V. Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. 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Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. 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K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. 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S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. 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Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. 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Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. 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Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). 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Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. 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Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. 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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. 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Ai-based tools for the diagnosis and treatment of rare neurological disorders. Nature Reviews Neurology 19, 455–456 (2023). [9] Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Richens, J. G., Lee, C. M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nature communications 11, 3923 (2020). [10] Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. 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[21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. 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Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. 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Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. 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[20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). 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IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. 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New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. 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Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. 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IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. 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Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. 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A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). 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[18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. 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Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. 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Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. 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In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. 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Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. 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IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nature medicine 29, 2307–2316 (2023). [11] Zhang, X., Wu, C., Zhang, Y., Xie, W. & Wang, Y. Knowledge-enhanced visual-language pre-training on chest radiology images. Nature Communications 14, 4542 (2023). [12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). 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[21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. 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Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. 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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. 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K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. 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Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. 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[17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. 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Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. 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Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020).
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[12] Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Radford, A. et al. Learning transferable visual models from natural language supervision. International conference on machine learning (PMLR) 8748–8763 (2021). [13] Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). [14] Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. 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M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. 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[33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. 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[21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. 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[33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). 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Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. 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Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). 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[21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. 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Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Chae, A. et al. Strategies for implementing machine learning algorithms in the clinical practice of radiology. Radiology 310, e223170 (2024). [15] Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. Ai in health and medicine. Nature medicine 28, 31–38 (2022). [16] Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ digital medicine 5, 48 (2022). [17] Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Jager, M. J. et al. Uveal melanoma. Nature reviews Disease primers 6, 24 (2020). [18] Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Shields, C. L. et al. Metastatic tumours to the eye. review of metastasis to the iris, ciliary body, choroid, retina, optic disc, vitreous, and/or lens capsule. Eye 37, 809–814 (2023). [19] Singh, A. D., Turell, M. E. & Topham, A. K. Uveal melanoma: trends in incidence, treatment, and survival. Ophthalmology 118, 1881–1885 (2011). [20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. 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[32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. 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Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. 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Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. 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New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). 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[22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. 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Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. 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Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Khoja, L. et al. 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[20] Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). 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L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. 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S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. 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Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. 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Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. 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[32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Kaliki, S., Shields, C. L. & Shields, J. A. Uveal melanoma: estimating prognosis. Indian journal of ophthalmology 63, 93 (2015). [21] Egan, K. M., Seddon, J. M., Glynn, R. J., Gragoudas, E. S. & Albert, D. M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. 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M. Epidemiologic aspects of uveal melanoma. Survey of ophthalmology 32, 239–251 (1988). [22] Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. Metastatic disease from uveal melanoma: treatment options and future prospects. British Journal of Ophthalmology (2016). [24] Khoja, L. et al. Meta-analysis in metastatic uveal melanoma to determine progression free and overall survival benchmarks: an international rare cancers initiative (irci) ocular melanoma study. Annals of Oncology 30, 1370–1380 (2019). [25] Mathis, T. et al. New concepts in the diagnosis and management of choroidal metastases. Progress in retinal and eye research 68, 144–176 (2019). [26] Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. Journal of machine learning research 9 (2008). [36] Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. 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Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proc. of the IEEE international conference on computer vision 618–626 (2017). [37] Wu, Y. et al. Interpretable identification of interstitial lung disease (ild) associated findings from ct. Medical Image Computing and Computer Assisted Intervention 560–569 (2020). [38] Tjoa, E. & Guan, C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems 32, 4793–4813 (2020). [39] Kostick-Quenet, K. M. & Gerke, S. Ai in the hands of imperfect users. npj Digital Medicine 5, 197 (2022). [40] Char, D. S., Abràmoff, M. D. & Feudtner, C. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics 20, 7–17 (2020). Augsburger, J. J. & Gamel, J. W. Clinical prognostic factors in patients with posterior uveal malignant melanoma. Cancer 66, 1596–1600 (1990). [23] Carvajal, R. D. et al. 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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. 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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. 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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). [33] Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: Contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022). [34] Zhang, S. et al. Large-scale domain-specific pretraining for biomedical vision-language processing. arXiv preprint arXiv:2303.00915 (2023). [35] Van der Maaten, L. & Hinton, G. 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International conference on machine learning (PMLR) 6105–6114 (2019). [27] Safari, P., India, M. & Hernando, J. Self-attention encoding and pooling for speaker recognition. arXiv preprint arXiv:2008.01077 (2020). [28] Achiam, J. et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023). [29] Kim, B. et al. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). International conference on machine learning (PMLR) 2668–2677 (2018). [30] Koh, P. W. et al. Concept bottleneck models. International conference on machine learning (PMLR) 5338–5348 (2020). [31] Yuksekgonul, M., Wang, M. & Zou, J. Post-hoc concept bottleneck models. International conference on learning representations (ICLR) (2023). [32] Yang, Y. et al. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 19187–19197 (2023). 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