Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V (2310.19061v1)

Abstract: In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal LLM, i.e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task. Our experiments thoroughly assess GPT-4V's proficiency in answering questions paired with images using both pathology and radiology datasets from 11 modalities (e.g. Microscopy, Dermoscopy, X-ray, CT, etc.) and fifteen objects of interests (brain, liver, lung, etc.). Our datasets encompass a comprehensive range of medical inquiries, including sixteen distinct question types. Throughout our evaluations, we devised textual prompts for GPT-4V, directing it to synergize visual and textual information. The experiments with accuracy score conclude that the current version of GPT-4V is not recommended for real-world diagnostics due to its unreliable and suboptimal accuracy in responding to diagnostic medical questions. In addition, we delineate seven unique facets of GPT-4V's behavior in medical VQA, highlighting its constraints within this complex arena. The complete details of our evaluation cases are accessible at https://github.com/ZhilingYan/GPT4V-Medical-Report.

Multimodal ChatGPT for Medical Applications: An Evaluation of GPT-4V

The paper "Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V" presents a critical evaluation of GPT-4V, a state-of-the-art multimodal LLM, focusing on its capabilities in Visual Question Answering (VQA) within the medical domain. This paper provides a comprehensive analysis of the model's performance across various medical imaging modalities and clinical inquiries, aiming to assess its reliability and accuracy in potential real-world diagnostic scenarios.

Evaluation Setup and Methodology

The authors conducted their assessment utilizing datasets derived from 11 distinct imaging modalities, including Microscopy, X-ray, and CT, encompassing 15 objects of interest like the brain, liver, and lungs. Medical inquiries were classified into 16 categories, covering a range of diagnostic questions. This extensive dataset was integral to testing the capabilities of GPT-4V on both pathology and radiology images within a VQA framework. Moreover, textual prompts were devised to guide GPT-4V in integrating visual and textual data efficiently. However, the paper concludes with the recommendation against employing GPT-4V for real-world diagnostics due to its suboptimal and unreliable diagnostic accuracy.

Key Findings and Observations

The experimental results indicate several compelling facets of GPT-4V's operational behavior:

1. Recognition of Imaging Modalities: GPT-4V exhibits proficiency in identifying various imaging modalities and their associated anatomical structures. This initial recognition sets the basis for further explorative analysis and diagnostics across broader medical applications.
2. Localization Challenges: Accurate localization, a crucial component in medical diagnostics, presents difficulty for GPT-4V, especially without explicit cues or context regarding image orientations. This shortfall has implications for interpreting complex medical images, which rely heavily on precise localization of tissues and abnormalities.
3. Size Assessment Difficulties: Determining the size of regions of interest, particularly in CT scans with multiple slices, is another significant challenge noted. GPT-4V often struggles with object size estimation, which is crucial for effective clinical diagnostics and treatment planning.
4. Visual and Linguistic Biases: While capable of joint text-image interpretations, GPT-4V demonstrates tendencies to rely excessively on textual descriptions or visual cues, leading to skewed or incomplete diagnostic outcomes.
5. Limitations for Diagnostics: With accuracy rates in radiology and pathology being unsatisfactorily low, the paper emphasizes that GPT-4V is currently unsuitable for clinical diagnostics. This observation is crucial given the potentially severe consequences of incorrect medical diagnosis.
6. Cautious, Thorough Responses: Despite the accuracy concerns, GPT-4V provides cautious responses, frequently emphasizing its status as non-diagnostic in nature. Its outputs are usually thorough, offering detailed explanations, albeit requiring validation by medical professionals for factual accuracy.

Implications and Future Directions

The findings elucidate fundamental limitations of GPT-4V in its current form, suggesting that the system, while promising, needs further advancements before being deemed reliable for practical medical applications. This underscores the critical need for improvements in multimodal integration, enhanced model training with diverse datasets, and meticulous prompt engineering. The comprehensive understanding of these issues paves the way for developing more sophisticated AI systems capable of addressing the multifaceted demands of medical diagnostics.

Future developments in AI could explore robust and seamless integration of visual and textual data, advanced training paradigms leveraging real-world medical cases, and collaboration with healthcare professionals to mitigate biases and enhance interpretability. Such efforts are expected to bridge existing gaps and maximize the potential impact of AI in transforming healthcare diagnostics, fostering an era where intelligent multimodal systems might one day work alongside medical professionals to achieve superior patient outcomes.