ClinicalAgents：具有双记忆的临床决策多智能体编排
ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory
AI总结：
提出ClinicalAgents多智能体框架，通过蒙特卡洛树搜索动态编排和双记忆架构模拟临床推理，显著提升诊断准确性和可解释性。
摘要：
虽然大型语言模型（LLMs）在医疗保健领域展现出潜力，但它们往往难以应对临床准确诊断所需的复杂非线性推理。现有方法通常依赖从症状到诊断的静态线性映射，未能捕捉人类临床医生固有的迭代、假设驱动推理。为弥补这一差距，我们引入了ClinicalAgents，一种新颖的多智能体框架，旨在模拟专家临床医生的认知工作流。与僵化的顺序链不同，ClinicalAgents采用了一种动态编排机制，建模为蒙特卡洛树搜索（MCTS）过程。这使得编排器能够迭代生成假设、主动验证证据，并在关键信息缺失时触发回溯。该框架的基础是双记忆架构：一个可变的短期工作记忆，用于维护不断演变的患者状态以进行上下文感知推理；以及一个静态的经验记忆，通过主动反馈循环检索临床指南和历史病例。大量实验表明，ClinicalAgents在评估的基线中取得了最佳性能，与强大的单智能体和多智能体基线相比，显著提高了诊断准确性和可解释性。我们的代码发布在https://github.com/ZhuohanGe/ClinicalAgents-Code。
英文摘要：
While Large Language Models (LLMs) have demonstrated potential in healthcare, they often struggle with the complex, non-linear reasoning required for accurate clinical diagnosis. Existing methods typically rely on static, linear mappings from symptoms to diagnoses, failing to capture the iterative, hypothesis-driven reasoning inherent in human clinicians. To bridge this gap, we introduce ClinicalAgents, a novel multi-agent framework designed to simulate the cognitive workflow of expert clinicians. Unlike rigid sequential chains, ClinicalAgents employs a dynamic orchestration mechanism modeled as a Monte Carlo Tree Search (MCTS) process. This allows an orchestrator to iteratively generate hypotheses, actively verify evidence, and trigger backtracking when critical information is missing. The foundation of this framework is a Dual-Memory architecture: a mutable working memory that maintains the evolving patient state for context-aware reasoning, and a static experience memory that retrieves clinical guidelines and historical cases via an active feedback loop. Extensive experiments demonstrate that ClinicalAgents achieves the best performance among evaluated baselines, significantly enhancing both diagnostic accuracy and explainability compared to strong single-agent and multi-agent baselines. Our code is released at https://github.com/ZhuohanGe/ClinicalAgents-Code.
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。
