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BianCang: A Traditional Chinese Medicine Large Language Model.

Sibo Wei, Xueping Peng, Yifei Wang

    IEEE Journal of Biomedical and Health Informatics
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    This study introduces BianCang, a specialized large language model (LLM) for traditional Chinese medicine (TCM). BianCang enhances TCM diagnosis and syndrome differentiation by integrating domain-specific knowledge and targeted training.

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    Area of Science:

    • Artificial Intelligence in Medicine
    • Traditional Chinese Medicine (TCM)

    Background:

    • Large language models (LLMs) show promise in medicine but struggle with TCM due to theoretical differences and data scarcity.
    • Existing medical LLMs lack specialized corpora for TCM diagnosis and syndrome differentiation.

    Purpose of the Study:

    • To develop a TCM-specific LLM, BianCang, to address the limitations of current models in TCM applications.
    • To improve the diagnostic and differentiation capabilities of LLMs within the context of TCM.

    Main Methods:

    • A two-stage training process was employed, involving domain-specific knowledge injection and targeted alignment.
    • Utilized pre-training corpora, instruction-aligned datasets from hospital records, and the ChP-TCM dataset.
    • Compiled extensive TCM and medical corpora for continual pre-training and supervised fine-tuning.

    Main Results:

    • Evaluations across 11 test sets, 31 models, and 4 tasks demonstrated BianCang's effectiveness.
    • BianCang shows significant improvements in TCM diagnosis and syndrome differentiation tasks.
    • The developed model offers valuable insights for advancing AI in TCM.

    Conclusions:

    • BianCang represents a significant advancement in applying LLMs to traditional Chinese medicine.
    • The proposed training methodology and datasets enhance LLM performance in specialized medical domains.
    • The availability of code, datasets, and models facilitates future research and development in AI-powered TCM.