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ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs (2305.15964v5)

Published 25 May 2023 in cs.CV

Abstract: The integration of Computer-Aided Diagnosis (CAD) with LLMs presents a promising frontier in clinical applications, notably in automating diagnostic processes akin to those performed by radiologists and providing consultations similar to a virtual family doctor. Despite the promising potential of this integration, current works face at least two limitations: (1) From the perspective of a radiologist, existing studies typically have a restricted scope of applicable imaging domains, failing to meet the diagnostic needs of different patients. Also, the insufficient diagnostic capability of LLMs further undermine the quality and reliability of the generated medical reports. (2) Current LLMs lack the requisite depth in medical expertise, rendering them less effective as virtual family doctors due to the potential unreliability of the advice provided during patient consultations. To address these limitations, we introduce ChatCAD+, to be universal and reliable. Specifically, it is featured by two main modules: (1) Reliable Report Generation and (2) Reliable Interaction. The Reliable Report Generation module is capable of interpreting medical images from diverse domains and generate high-quality medical reports via our proposed hierarchical in-context learning. Concurrently, the interaction module leverages up-to-date information from reputable medical websites to provide reliable medical advice. Together, these designed modules synergize to closely align with the expertise of human medical professionals, offering enhanced consistency and reliability for interpretation and advice. The source code is available at https://github.com/zhaozh10/ChatCAD.

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A Review of "ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs"

The paper "ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs" presents an intriguing approach to integrating LLMs into computer-aided diagnosis (CAD) systems. It focuses on overcoming existing limitations in merging LLMs with CAD, such as restricted imaging domain scope and inadequate medical expertise of LLMs. The proposed solution, ChatCAD+, emphasizes universality and reliability, situating itself as an interactive CAD system capable of generating dependable diagnostics and interacting effectively with medical professionals and patients.

One of the central components of ChatCAD+ includes the integration of multi-domain CAD models, which addresses the limitation of previous systems confined to specific imaging modalities. By incorporating a domain identification module, ChatCAD+ can process various medical images, identifying the appropriate CAD network tailored to a specific domain. This adaptability enhances the system's generalizability across diverse clinical environments. In particular, the use of BiomedCLIP for domain identification illustrates an effective adaptation of existing deep learning architectures to uniquely meet medical challenges.

Moreover, the system employs a hierarchical in-context learning mechanism to uplift the quality of report generation. The proposed structure retrieves and utilizes semantically similar reports as in-context examples for refining initial report drafts generated by LLMs. Through this dual-stage generation process—preliminary report generation followed by template-driven enhancement—ChatCAD+ substantially improves the coherency, relevance, and precision of its diagnostics compared to earlier models. The paper provides detailed numerical results confirming improvements in NLG metrics (BLEU, ROUGE-L, METEOR) and clinical efficacy metrics (precision, recall, F1-score), underscoring the practical advantages of their approach.

Furthermore, ChatCAD+ implements a robust LLM-based knowledge retrieval system for reliable interactions. This component leverages external databases, such as the Merck Manuals, to furnish diagnostically accurate and contextually aware responses to clinical queries. The adoption of LLM as a tool to recursively search relevant knowledge across hierarchically organized medical topics demonstrates a refined methodology for ensuring responses align with clinical realities, thus enhancing patient trust and understanding.

The implications of this research are substantial. Practically, ChatCAD+ increases the accessibility and reliability of automated medical consultation, potentially easing the workload of healthcare providers and enhancing patient self-care. Theoretically, it presents a viable framework for continuously integrating emerging medical knowledge into AI-driven diagnostic tools—aligning closely with the dynamic nature of clinical medicine. However, the reliance on external databases and the inherent requirement for substantial computational resources present areas for future development. Addressing these limitations could lead to broader adoption and integration into existing healthcare systems.

In conclusion, the paper establishes a convincing case for the deployment of specialized CAD models alongside LLMs to achieve both breadth and depth in medical diagnostics. With advancements in prompt designs and multi-module systems such as ChatCAD+, the intersection of LLMs and CAD is poised to transform digital healthcare delivery, allowing for more personalized and scalable medical diagnostics. Future directions may include expanding the range of integrated medical knowledge bases and improving the efficiency of model components to reduce computational overhead. These advancements will likely encourage further research and application in AI-driven healthcare solutions.

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Authors (9)
  1. Zihao Zhao (42 papers)
  2. Sheng Wang (239 papers)
  3. Jinchen Gu (1 paper)
  4. Yitao Zhu (11 papers)
  5. Lanzhuju Mei (5 papers)
  6. Zixu Zhuang (6 papers)
  7. Zhiming Cui (34 papers)
  8. Qian Wang (453 papers)
  9. Dinggang Shen (153 papers)
Citations (25)
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  1. GitHub - zhaozh10/ChatCAD: [COMMSENG'24, TMI'24] Interactive Computer-Aided Diagnosis using LLMs (113 stars)