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ZhiFangDanTai: Fine-Tuning Graph-Based Retrieval-Augmented Generation Model for Traditional Chinese Medicine Formula.

Zixuan Zhang, Bowen Hao, Yingjie Li

    IEEE Journal of Biomedical and Health Informatics
    |December 17, 2025
    PubMed
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    ZhiFang DanTai enhances Traditional Chinese Medicine (TCM) formula generation by integrating Graph Retrieval Augmented Generation (GraphRAG) with Large Language Models (LLMs). This approach improves explainability and reduces errors in complex disease treatment models.

    Area of Science:

    • Computational Medicine
    • Artificial Intelligence in Healthcare
    • Traditional Chinese Medicine

    Background:

    • Traditional Chinese Medicine (TCM) formulas are crucial for treating epidemics and complex diseases.
    • Existing computational models for TCM lack comprehensive formula details and explanations.
    • Current Large Language Models (LLMs) fine-tuned on TCM data suffer from insufficient detail, limiting output depth.

    Purpose of the Study:

    • To propose ZhiFang DanTai, a novel framework combining Graph Retrieval Augmented Generation (GraphRAG) with LLM fine-tuning.
    • To enhance the explainability and accuracy of TCM formula generation.
    • To address limitations in existing TCM datasets and models.

    Main Methods:

    • Developed ZhiFang DanTai, a framework integrating GraphRAG with LLM fine-tuning.

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  • Utilized GraphRAG to retrieve and synthesize structured TCM knowledge.
  • Constructed an enhanced instruction dataset for improved LLM information integration.

    • Main Results:

    • ZhiFang DanTai demonstrated significant improvements over state-of-the-art models on collected and clinical datasets.
    • Theoretical proofs confirmed that GraphRAG and fine-tuning reduce generalization error and hallucination rates.
    • The framework generates more comprehensive TCM formula compositions and explanations.

    Conclusions:

    • ZhiFang DanTai effectively enhances TCM formula generation by leveraging GraphRAG and LLM fine-tuning.
    • The proposed method offers a significant advancement in explainable AI for Traditional Chinese Medicine.
    • The open-sourced model facilitates further research and application in computational medicine.