Metaphorical expressions are abundant in Traditional Chinese Medicine (TCM), conveying complex disease mechanisms and holistic health concepts through culturally rich and often abstract terminology. Bridging these metaphors to anatomically driven Western medical (WM) concepts poses significant challenges for both automated language processing and real-world clinical practice. To address this gap, we propose a novel multi-agent and chain-of-thought (CoT) framework designed to interpret TCM metaphors accurately and map them to WM pathophysiology. Specifically, our approach combines domain-specialized agents (TCM Expert, WM Expert) with a Coordinator Agent, leveraging stepwise chain-of-thought prompts to ensure transparent reasoning and conflict resolution. We detail a methodology for building a metaphor-rich TCM dataset, discuss strategies for effectively integrating multi-agent collaboration and CoT reasoning, and articulate the theoretical underpinnings that guide metaphor interpretation across distinct medical paradigms. We present a comprehensive system design and highlight both the potential benefits and limitations of our approach, while leaving placeholders for future experimental validation. Our work aims to support clinical decision-making, cross-system educational initiatives, and integrated healthcare research, ultimately offering a robust scaffold for reconciling TCM's symbolic language with the mechanistic focus of Western medicine.

翻译：隐喻表达在传统中医中极为丰富，通过富含文化内涵且通常较为抽象的术语，传达复杂的疾病机制和整体健康观念。将这些隐喻与解剖学驱动的西方医学概念相衔接，对自动化语言处理和现实临床实践均构成重大挑战。为弥合这一鸿沟，我们提出了一种新颖的多智能体与思维链框架，旨在准确解读中医隐喻并将其映射至西医病理生理学。具体而言，我们的方法结合了领域专家智能体（中医专家、西医专家）与协调智能体，利用逐步思维链提示确保推理过程的透明性与冲突解决。我们详细阐述了构建富含隐喻的中医数据集的方法论，讨论了有效整合多智能体协作与思维链推理的策略，并阐明了指导跨医学范式隐喻解读的理论基础。我们呈现了完整的系统设计，重点分析了该方法的潜在优势与局限性，同时为未来的实验验证预留了空间。本研究旨在支持临床决策、跨体系教育计划及整合医疗研究，最终为调和中医象征性语言与西医机制导向提供坚实的框架支撑。