用多智能体模拟医生问诊全过程，让AI像真人一样逐步诊断。
DynamiCare: A Dynamic Multi-Agent Framework for Interactive and Open-Ended Medical Decision-Making
- 构建多轮交互式诊疗框架，智能体动态调整策略与组合。
- 基于MIMIC-III数据创建新数据集MIMIC-Patient，支持真实场景模拟。
- 首个面向动态医疗决策的基准测试，适合医疗AI研究者参考。
大型语言模型（LLMs）推动了具备领域推理与交互能力的专业化AI代理发展，尤其在医疗领域。尽管现有框架可模拟医疗决策，但多局限于单轮任务，医生代理在初始即获得完整病历信息——这与现实诊疗中不确定性高、交互性强、反复追问的特性不符。本文提出基于MIMIC-III电子健康记录（EHRs）构建的结构化数据集MIMIC-Patient，用于支持患者级动态仿真。在此基础上，我们设计了DynamiCare：一种新型动态多智能体框架，将临床诊断建模为多轮交互循环，由专科智能体团队持续向患者系统提问、整合新信息，并动态调整自身组成与策略。通过大量实验验证了该框架的可行性与有效性，建立了首个基于LLM代理的动态临床决策基准。
原文摘要 · Abstract (English)
The rise of Large Language Models (LLMs) has enabled the development of specialized AI agents with domain-specific reasoning and interaction capabilities, particularly in healthcare. While recent frameworks simulate medical decision-making, they largely focus on single-turn tasks where a doctor agent receives full case information upfront -- diverging from the real-world diagnostic process, which is inherently uncertain, interactive, and iterative. In this paper, we introduce MIMIC-Patient, a structured dataset built from the MIMIC-III electronic health records (EHRs), designed to support dynamic, patient-level simulations. Building on this, we propose DynamiCare, a novel dynamic multi-agent framework that models clinical diagnosis as a multi-round, interactive loop, where a team of specialist agents iteratively queries the patient system, integrates new information, and dynamically adapts its composition and strategy. We demonstrate the feasibility and effectiveness of DynamiCare through extensive experiments, establishing the first benchmark for dynamic clinical decision-making with LLM-powered agents.
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