JingFang: An Expert-Level Large Language Model for Traditional Chinese Medicine Clinical Consultation and Syndrome Differentiation-Based Treatment
JingFang: An Expert-Level Large Language Model for Traditional Chinese Medicine Clinical Consultation and Syndrome Differentiation-Based Treatment
The effective application of traditional Chinese medicine (TCM) requires extensive knowledge of TCM and clinical experience. The emergence of Large Language Models (LLMs) provides a solution to this, while existing LLMs for TCM exhibit critical limitations of incomplete clinical consultation and diagnoses, as well as inaccurate syndrome differentiation. To address these issues, we establish JingFang (JF), a novel TCM LLM that demonstrates the level of expertise in clinical consultation and syndrome differentiation. We propose a Multi-Agent Collaborative Chain-of-Thought Mechanism (MACCTM) for comprehensive and targeted clinical consultation, enabling JF with effective and accurate diagnostic ability. In addition, a Syndrome Agent and a Dual-Stage Recovery Scheme (DSRS) are developed to accurately enhance the differentiation of the syndrome and the subsequent corresponding treatment. JingFang not only facilitates the application of LLMs but also promotes the effective application of TCM for healthcare.
Chisheng Li、Yehan Yang、Tianhao Ma、Ruotai Li、Xinhan Zheng、Guodong Shan
中医学医学研究方法
Chisheng Li,Yehan Yang,Tianhao Ma,Ruotai Li,Xinhan Zheng,Guodong Shan.JingFang: An Expert-Level Large Language Model for Traditional Chinese Medicine Clinical Consultation and Syndrome Differentiation-Based Treatment[EB/OL].(2025-02-03)[2025-08-02].https://arxiv.org/abs/2502.04345.点此复制
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