JingFang用多智能体系统提升中医问诊与辨证精准度。
Jingfang: An LLM-Based Multi-Agent System for Precise Medical Consultation and Syndrome Differentiation in Traditional Chinese Medicine
- 构建多智能体协作问诊机制，模拟真实中医诊断流程。
- 辨证准确率较现有模型提升124%，较SOTA模型提升21.1%。
- 适合需要高精度中医AI辅助的临床研究与诊疗场景。
传统中医诊疗依赖深厚专业知识与丰富临床经验。尽管大语言模型（LLMs）在此领域具潜力，现有面向中医的LLM仍存在两大缺陷：（1）咨询框架僵化，难以实现全面个性化交互，常导致诊断偏差；（2）治疗建议生成缺乏严谨辨证，偏离中医核心诊疗原则。为此，我们提出基于大模型的多智能体系统JingFang（JF），支持中医辅助诊断与治疗。JF整合多个符合真实中医诊疗场景的专科智能体，实现个性化问诊、精准辨证及治疗推荐。构建了针对中医的多智能体协同问诊机制（MACCM），使多个智能体协作模拟真实诊断流程，显著增强基础大模型的诊断能力。此外，引入专用于辨证的智能体（在预处理数据集上微调），并设计治疗智能体内的双阶段恢复方案（DSRS），共同大幅提升辨证与治疗准确性。综合评估显示，JF在医疗问诊中表现优异，辨证精度较现有中医模型提升至少124%，较SOTA大模型提升21.1%。
原文摘要 · Abstract (English)
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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