MedExAgent：训练LLM代理在嘈杂的临床环境中提问、检查和诊断
MedExAgent: Training LLM Agents to Ask, Examine, and Diagnose in Noisy Clinical Environments
AI总结：
本文提出MedExAgent，通过两阶段流程训练医疗诊断代理，结合合成对话和DAPO优化，实现高效诊断与成本控制。
摘要：
现实中的临床诊断是一个复杂过程，医生需通过与患者互动和进行医学检查获取信息，同时需适应不同患者类型及嘈杂不完整信息。现有医疗LLM基准和自动诊断方法多简化此过程为单轮问答、无噪声对话或顺序检查等，忽略临床诊断的交互性和不确定性。本文通过将临床诊断形式化为部分可观测马尔可夫决策过程（POMDP），引入三种动作类型：提问患者、调用医学检查和下达诊断。同时提出包含七种患者噪声类型和三种检查噪声类型的系统噪声模型。通过所提环境，训练出有效诊断代理MedExAgent，通过监督微调和DAPO优化复合奖励函数，涵盖诊断准确性、工具调用质量和检查成本。实验表明MedExAgent在诊断性能上与大模型相当，同时保持成本高效的检查策略。
英文摘要：
Real-world clinical diagnosis is a complex process in which the doctor is required to obtain information from both interaction with the patient and conducting medical exams. Additionally, the doctor needs to adapt to different patient personas, as well as noisy and incomplete information that can happen at any time during the process. However, existing benchmarks for medical LLMs and methods for automatic diagnosis largely simplify this process by reducing it to single-turn question answering, noise-free conversations, or sequential exam making, etc., ignoring the interactive and uncertain nature of clinical diagnosis. In this paper, we aim to address this gap by formalizing clinical diagnosis as a Partially Observable Markov Decision Process (POMDP) with three action types: questioning the patient, ordering medical exams as tool calls, and issuing a diagnosis. We also introduce a systematic noise model comprising seven patient noise types and three exam noise types. Using our proposed environment, we train an effective diagnosis agent, \textbf{MedExAgent}, through a two-stage pipeline that first performs supervised finetuning on synthetic conversations structured after the Calgary-Cambridge model for clinical interviews, and then applies DAPO to optimize a composite reward capturing diagnostic accuracy, tool call quality, and exam cost including financial cost and patient discomfort. Through extensive experiments and ablation studies, we demonstrate that MedExAgent achieves diagnostic performance comparable to larger models while maintaining cost-efficient examination strategies.
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。
