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An LLM Method for Understanding Traditional Chinese Medicine: Mechanism Exploration and Innovative Application.

Yuan-Xin Li, Said Elnaffar, Hong-Yi Chen

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    This summary is machine-generated.

    This study introduces a novel Large Language Model (LLM) framework for Traditional Chinese Medicine (TCM), enhancing personalized treatment and clinical applications. The AI model effectively captures TCM principles, improving diagnostic logic and paving the way for AI-driven personalized medicine.

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    Area of Science:

    • Artificial Intelligence
    • Computational Medicine
    • Traditional Chinese Medicine

    Background:

    • Large Language Models (LLMs) show potential in medical knowledge representation but face challenges in dynamic clinical workflows and personalized treatments within complex systems like Traditional Chinese Medicine (TCM).
    • Modeling the core TCM principle of "different treatments for the same disease" remains a significant challenge for AI.
    • Existing AI frameworks struggle to capture the nuanced, school-specific diagnostic logic inherent in TCM.

    Purpose of the Study:

    • To develop an efficient and novel LLM framework for exploring TCM mechanisms and clinical applications.
    • To address the limitations of current LLMs in handling the complexity and personalization required for TCM.
    • To create an AI model capable of understanding and applying the principle of "different treatments for the same disease".

    Main Methods:

    • A two-stage LLM framework combining incremental domain-specific pre-training, multi-task supervised fine-tuning, and Chain-of-Thought (CoT) reasoning.
    • Leveraging a heterogeneous database of 100,538 records from 19 TCM physicians for model training.
    • Utilizing six downstream tasks, including personalized prescription generation, to assess clinical capabilities.

    Main Results:

    • The LLM framework demonstrated a 1,313% improvement in BLEU-4 score after incremental pre-training, reaching 41.26-43.21 after fine-tuning.
    • Formal validation confirmed the critical role of basic formulas, with their removal causing a 23.9% performance drop.
    • Cross-school evaluations showed robust generalization capabilities, achieving 22.8 BLEU-4 on external data. CoT annotation improved performance by 20% with only 10% of labeled data.

    Conclusions:

    • The proposed LLM framework successfully captures TCM's "different treatments for the same disease" principle and preserves school-specific diagnostic logic.
    • The model demonstrates high data efficiency and robust generalization, advancing intelligent TCM inheritance.
    • This work paves the way for AI-driven personalized medicine by enhancing TCM's clinical application and knowledge representation.