从医生专业能力到临床代理：通过轻量级大语言模型保留、标准化和扩展医生的医学专业知识
From Physician Expertise to Clinical Agents: Preserving, Standardizing, and Scaling Physicians' Medical Expertise with Lightweight LLM
AI总结：
本文提出Med-Shicheng框架，通过轻量级大语言模型系统学习并转移知名中医的辨证论治理念及案例依赖适应规则，实现医学知识的标准化和规模化应用。
摘要：
医学是一门通过长期观察和临床实践的复杂现实精炼而成的经验学科。医生通过反复的应用、反思和改进过程，形成个性化的方法体系。然而，结果差异较大，资深医生的知识体系发展缓慢且难以大规模传播，导致高质量临床专业知识稀缺。为此，我们提出Med-Shicheng，一个通用框架，使大语言模型能够系统地学习和转移知名医生的辨证论治哲学及案例依赖的适应规则。基于Tianyi，Med-Shicheng包含五个阶段。我们针对五位国家级中医大师或知名中医师，整理多源材料，并训练一个模型以在七个任务中内化所有五个知识体系，包括病因病机分析、证候诊断、治疗原则选择、方剂生成、方剂解释、症状演变与方剂调整以及临床建议。在Qwen2.5-1.5B-Base上实现，Med-Shicheng在资源受限的GPU上实现了与DeepSeek-R1和GPT-5相当的性能。我们还检验了LLM作为评判者与医生评估的可靠性：自动化评判追踪整体趋势但对细粒度个性化区别存在偏差，凸显了在地面真实不可用时需要医生参与以及领域适应的评判模型的重要性。
英文摘要：
Medicine is an empirical discipline refined through long-term observation and the messy, high-variance reality of clinical practice. Physicians build diagnostic and therapeutic competence through repeated cycles of application, reflection, and improvement, forming individualized methodologies. Yet outcomes vary widely, and master physicians' knowledge systems are slow to develop and hard to transmit at scale, contributing to the scarcity of high-quality clinical expertise. To address this, we propose Med-Shicheng, a general framework that enables large language models to systematically learn and transfer distinguished physicians' diagnostic-and-therapeutic philosophy and case-dependent adaptation rules in a standardized way. Built on Tianyi, Med-Shicheng consists of five stages. We target five National Masters of Chinese Medicine or distinguished TCM physicians, curate multi-source materials, and train a single model to internalize all five knowledge systems across seven tasks, including etiology-pathogenesis analysis, syndrome diagnosis, treatment principle selection, prescription generation, prescription explanation, symptom evolution with regimen adjustment, and clinical advice. Implemented on Qwen2.5-1.5B-Base, Med-Shicheng runs on resource-constrained GPUs while achieving performance comparable to DeepSeek-R1 and GPT-5. We also examine the reliability of LLM-as-a-judge versus physician evaluation: automated judging tracks overall trends but shows bias on fine-grained individualized distinctions, highlighting the need for physician involvement when ground truth is unavailable and for domain-adapted judge models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
