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Jingfang: An LLM-Based Multi-Agent System for Precise Medical Consultation and Syndrome Differentiation in Traditional Chinese Medicine
Yehan Yang, Tianhao Ma, Ruotai Li, Xinhan Zheng, Guodong Shan · 2025-02-04 · via cs.CL updates on arXiv.org
AI 总结

  1. 现有面向中医的大型语言模型存在两个关键缺陷：僵化的咨询框架无法进行全面的个性化问诊，常导致诊断偏差；治疗建议缺乏严格的辨证论治，偏离中医核心诊疗原则。为解决这些问题，研究团队开发了基于大语言模型的多智能体系统JingFang（JF），通过整合多个中医专科智能体，模拟真实诊疗场景，实现个性化医疗咨询、精准辨证和推荐治疗方案。

  2. JingFang系统构建了中医多智能体协同咨询机制（MACCM），多个智能体协作模拟真实的中医诊断流程，提升基础大语言模型的诊断能力，以提供准确且个性化的医疗服务。系统还引入了专用的辨证智能体，该智能体在预处理数据集上进行了微调，并在治疗智能体内设计了双阶段恢复方案（DSRS），共同显著提高了模型在辨证和治疗方面的准确性。

  3. 综合评估和实验表明，JingFang在医疗咨询方面表现出优越性能。与现有中医模型相比，其辨证精确度提升了至少124%；与最先进的大语言模型（SOTA LLMs）相比，精确度也提升了至少21.1%。这些结果证实了多智能体协同和专用模块设计的有效性，为人工智能辅助中医诊疗提供了可行方案。

The practice of Traditional Chinese Medicine (TCM) requires profound expertise and extensive clinical experience. While Large Language Models (LLMs) offer significant potential in this domain, current TCM-oriented LLMs suffer two critical limitations: (1) a rigid consultation framework that fails to conduct comprehensive and patient-tailored interactions, often resulting in diagnostic inaccuracies; and (2) treatment recommendations generated without rigorous syndrome differentiation, which deviates from the core diagnostic and therapeutic principles of TCM. To address these issues, we develop \textbf{JingFang (JF)}, an advanced LLM-based multi-agent system for TCM that facilitates the implementation of AI-assisted TCM diagnosis and treatment. JF integrates various TCM Specialist Agents in accordance with authentic diagnostic and therapeutic scenarios of TCM, enabling personalized medical consultations, accurate syndrome differentiation and treatment recommendations. A \textbf{Multi-Agent Collaborative Consultation Mechanism (MACCM)} for TCM is constructed, where multiple Agents collaborate to emulate real-world TCM diagnostic workflows, enhancing the diagnostic ability of base LLMs to provide accurate and patient-tailored medical consultation. Moreover, we introduce a dedicated \textbf{Syndrome Differentiation Agent} fine-tuned on a preprocessed dataset, along with a designed \textbf{Dual-Stage Recovery Scheme (DSRS)} within the Treatment Agent, which together substantially improve the model's accuracy of syndrome differentiation and treatment. Comprehensive evaluations and experiments demonstrate JF's superior performance in medical consultation, and also show improvements of at least 124% and 21.1% in the precision of syndrome differentiation compared to existing TCM models and State of the Art (SOTA) LLMs, respectively.

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