AI 中文总结
本研究构建基于DeepSeek V3.2的多专家智能体框架DeepTCM1.0，以桂枝汤为案例，结合中医理论与现代科学解析中药复方机制，通过多维度评估验证框架性能。
AI 中文摘要
背景：中药（TCM）复方的机制阐释仍是中药现代化的核心挑战。传统方法包括数据挖掘和网络药理学，不足以实现经典中医理论与现代科学研究的深度融合。此外，使用通用人工智能大语言模型进行直接问答，受限于对中医理论框架的适配不足及易出现推理幻觉的问题。因此，亟需开发符合中医整体原则的智能分析方法。目的：建立融合经典中医理论与现代生命科学的多专家智能体框架，实现对中药复方的系统、可解释的机制分析，并以桂枝汤作为代表性验证案例。方法：DeepTCM1.0框架基于通用大语言模型DeepSeek V3.2构建，采用三层协作架构和三轮迭代质量控制工作流，模拟11个跨学科智能体的协作分析过程。该框架从经典中医理论与现代科学研究双视角应用于桂枝汤的机制阐释。通过双盲五维评分、组内相关系数（ICC）可靠性测试、曼-惠特尼U检验及效应量分析对框架性能进行综合评估，评估使用4个独立大语言模型作为评估者，每个评估者对5份匿名报告进行5轮重复评分，共完成100次独立评分评估。
英文摘要
Background: Mechanistic elucidation of traditional Chinese medicine (TCM) compound formulas remains a central challenge in the modernization of TCM. Conventional approaches, including data mining and network pharmacology, are insufficient for achieving deep integration between classical TCM theory and modern scientific research. In addition, direct question-answering using general-purpose artificial intelligence large language models is limited by inadequate adaptation to TCM theoretical frameworks and susceptibility to reasoning hallucinations. Consequently, there is an urgent need to develop intelligent analytical methods aligned with the holistic principles of TCM. Objective: To establish a multi-expert intelligent agent framework integrating classical TCM theory with modern life sciences, thereby enabling systematic and interpretable mechanistic analysis of TCM compound formulas, with Guizhi Decoction serving as a representative validation case. Methods: The DeepTCM1.0 framework was constructed based on the general-purpose large language model DeepSeek V3.2. It adopts a three-tier collaborative architecture and a three-round iterative quality-control workflow, simulating the collaborative analytical process of 11 interdisciplinary intelligent agents. The framework was applied to the mechanistic interpretation of Guizhi Decoction from the dual perspectives of classical traditional Chinese medicine theory and modern scientific research. Framework performance was comprehensively evaluated through double-blind five-dimensional scoring, intraclass correlation coefficient (ICC) reliability testing, Mann-Whitney U tests, and effect size analysis. The evaluation employed four independent large language models as evaluators, each conducting five rounds of repeated scoring on five anonymized reports, resulting in a total of 100 independent scoring assessments.
