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ChatCAD+: Toward a Universal and Reliable Interactive CAD Using LLMs.

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    ChatCAD+ enhances medical AI by integrating Computer-Aided Diagnosis (CAD) and Large Language Models (LLMs). It offers reliable medical image interpretation and trustworthy virtual consultations, improving diagnostic accuracy and patient advice.

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    Area of Science:

    • Artificial Intelligence in Medicine
    • Medical Imaging Analysis
    • Clinical Decision Support Systems

    Background:

    • Computer-Aided Diagnosis (CAD) and Large Language Models (LLMs) integration shows promise for automating radiology tasks and virtual consultations.
    • Current LLM applications in medicine are limited by narrow imaging domain scope and insufficient diagnostic capabilities, impacting report reliability.
    • LLMs lack deep medical expertise, hindering their effectiveness as virtual family doctors due to unreliable advice.

    Purpose of the Study:

    • To introduce ChatCAD+, a universal and reliable system addressing limitations in current CAD and LLM integration for clinical applications.
    • To enhance the reliability and scope of AI-driven medical report generation and patient consultations.

    Main Methods:

    • Developed ChatCAD+ with two core modules: Reliable Report Generation and Reliable Interaction.
    • Implemented hierarchical in-context learning for interpreting diverse medical imaging domains.
    • Integrated up-to-date information from reputable medical websites for reliable patient advice.

    Main Results:

    • ChatCAD+ demonstrates capability in interpreting medical images across various domains.
    • The system generates high-quality medical reports with enhanced consistency and reliability.
    • The interaction module provides trustworthy medical advice, mimicking human medical professional expertise.

    Conclusions:

    • ChatCAD+ offers a significant advancement in AI-driven clinical support, providing universal and reliable diagnostic interpretation and consultation.
    • The system's synergistic modules enhance consistency and reliability, aligning AI capabilities with human medical expertise.
    • The developed system aims to improve the quality and trustworthiness of AI in healthcare.