重塑临床对话：基于大语言模型的智能体范式在医疗沟通中的创新应用

Reinventing Clinical Dialogue: Agentic Paradigms for LLM Enabled Healthcare Communication

December 1, 2025

作者: Xiaoquan Zhi, Hongke Zhao, Likang Wu, Chuang Zhao, Hengshu Zhu

cs.AI

摘要

临床对话呈现出复杂的二元性：既需要自然交流中的人文温度，又要求循证医学的严谨精确。尽管大语言模型具备前所未有的语言能力，但其依赖被动响应和无状态处理的架构特性，往往更偏向概率合理性而非事实准确性。这一结构性局限正推动医学人工智能从生成式文本预测向智能体自主性范式转变——模型作为核心推理引擎，能够进行审慎规划并保持持续记忆。相较于现有研究主要罗列下游应用，本综述从第一性原理出发解析支撑这一转变的认知架构。我们提出沿知识来源与智能体目标两条正交轴构建的新型分类法，用以界定临床知识的溯源与系统操作范畴的对应关系。该框架通过将方法划分为四大原型（潜在空间临床医师、涌现规划器、 grounded 知识合成器、可验证工作流自动化器），系统剖析创造力与可靠性之间的内在权衡。针对每种范式，我们解构其在完整认知管道（涵盖战略规划、记忆管理、行动执行、协作演进）中的技术实现，揭示不同架构选择如何平衡自主性与安全性之间的张力。

English

Clinical dialogue represents a complex duality requiring both the empathetic fluency of natural conversation and the rigorous precision of evidence-based medicine. While Large Language Models possess unprecedented linguistic capabilities, their architectural reliance on reactive and stateless processing often favors probabilistic plausibility over factual veracity. This structural limitation has catalyzed a paradigm shift in medical AI from generative text prediction to agentic autonomy, where the model functions as a central reasoning engine capable of deliberate planning and persistent memory. Moving beyond existing reviews that primarily catalog downstream applications, this survey provides a first-principles analysis of the cognitive architecture underpinning this shift. We introduce a novel taxonomy structured along the orthogonal axes of knowledge source and agency objective to delineate the provenance of clinical knowledge against the system's operational scope. This framework facilitates a systematic analysis of the intrinsic trade-offs between creativity and reliability by categorizing methods into four archetypes: Latent Space Clinicians, Emergent Planners, Grounded Synthesizers, and Verifiable Workflow Automators. For each paradigm, we deconstruct the technical realization across the entire cognitive pipeline, encompassing strategic planning, memory management, action execution, collaboration, and evolution to reveal how distinct architectural choices balance the tension between autonomy and safety.