用多智能体模拟问诊流程，提升动态诊断准确性
Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis
- 构建基于问诊流程的多智能体框架，分层设计动作策略
- 在公开基准上超越基线模型，达到当前最佳性能
- 适合医疗AI系统研发者和临床决策支持研究者
传统医疗AI系统多依赖单一模态数据，因信息不完整而限制诊断准确率。尽管基础模型在融合多模态信息方面展现潜力，但在动态诊断任务中表现不佳，难以处理多轮交互，常因信息收集不足而过早做出诊断。为此，我们提出一种受问诊流程与强化学习启发的多智能体框架，模拟完整问诊过程，整合多源临床信息以实现高效诊断。该方法采用分层动作集，基于问诊流程与医学教材结构化设计，有效引导决策过程。此策略增强智能体间互动，使其能根据动态状态自适应优化行为。我们在公开的动态诊断基准上评估了该框架，结果表明其显著优于基线方法，并在现有基于基础模型的方法中达到最优表现。
原文摘要 · Abstract (English)
Traditional AI-based healthcare systems often rely on single-modal data, limiting diagnostic accuracy due to incomplete information. However, recent advancements in foundation models show promising potential for enhancing diagnosis combining multi-modal information. While these models excel in static tasks, they struggle with dynamic diagnosis, failing to manage multi-turn interactions and often making premature diagnostic decisions due to insufficient persistence in information collection.To address this, we propose a multi-agent framework inspired by consultation flow and reinforcement learning (RL) to simulate the entire consultation process, integrating multiple clinical information for effective diagnosis. Our approach incorporates a hierarchical action set, structured from clinic consultation flow and medical textbook, to effectively guide the decision-making process. This strategy improves agent interactions, enabling them to adapt and optimize actions based on the dynamic state. We evaluated our framework on a public dynamic diagnosis benchmark. The proposed framework evidentially improves the baseline methods and achieves state-of-the-art performance compared to existing foundation model-based methods.
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