医学大模型从查资料到会推理，提升诊疗决策透明度。
Reasoning LLMs in the Medical Domain: A Literature Survey
- 用思维链等提示技术让大模型学会医学推理
- 结合强化学习提升模型临床判断能力
- 适合医疗AI研究者与临床决策系统开发者
大型语言模型（LLMs）在推理能力上的进步，正在推动医疗应用的变革。这些模型不再只是信息检索工具，而是具备复杂医疗决策支持能力的临床推理系统。本综述深入分析了其技术基础，重点涵盖思维链（Chain-of-Thought）等专用提示技术及强化学习新进展（如DeepSeek-R1）。同时评估了专为医学设计的框架，探讨多智能体协作与新型提示架构等新兴范式。文章还批判性审视当前医学验证方法，讨论领域解释力局限、偏见缓解策略、患者安全框架以及多模态临床数据融合等挑战。旨在为构建可信赖的临床辅助型大模型提供发展路线图。
原文摘要 · Abstract (English)
The emergence of advanced reasoning capabilities in Large Language Models (LLMs) marks a transformative development in healthcare applications. Beyond merely expanding functional capabilities, these reasoning mechanisms enhance decision transparency and explainability-critical requirements in medical contexts. This survey examines the transformation of medical LLMs from basic information retrieval tools to sophisticated clinical reasoning systems capable of supporting complex healthcare decisions. We provide a thorough analysis of the enabling technological foundations, with a particular focus on specialized prompting techniques like Chain-of-Thought and recent breakthroughs in Reinforcement Learning exemplified by DeepSeek-R1. Our investigation evaluates purpose-built medical frameworks while also examining emerging paradigms such as multi-agent collaborative systems and innovative prompting architectures. The survey critically assesses current evaluation methodologies for medical validation and addresses persistent challenges in field interpretation limitations, bias mitigation strategies, patient safety frameworks, and integration of multimodal clinical data. Through this survey, we seek to establish a roadmap for developing reliable LLMs that can serve as effective partners in clinical practice and medical research.
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