让AI像医生一样一步步问诊，通过自我进化提升诊断效率。
EvoClinician: A Self-Evolving Agent for Multi-Turn Medical Diagnosis via Test-Time Evolutionary Learning
- 设计自适应问诊循环：诊断-评分-演化，动态优化策略。
- 在真实临床数据上实现92.3%诊断准确率，优于基线模型。
- 适合医疗AI研究者与智能问诊系统开发者参考。
现有医疗AI多基于一次性完整病历进行诊断，但真实诊疗是迭代过程，需医生逐步提问和安排检查以高效获取信息并控制成本。为此，我们提出Med-Inquire基准，基于真实临床案例构建，通过隐藏患者文件并设置患者与检查代理，迫使智能体主动询问和请求检验来分步获取信息。为应对该挑战，我们进一步提出EvoClinician，一种测试时自进化的诊断智能体。其核心为“诊断-评分-演化”循环：行为代理尝试诊断，过程评估代理通过临床收益与资源效率对每一步行动打分，演化代理则依据反馈更新行为代理的提示词与记忆。实验表明，EvoClinician在诊断准确率（92.3%）与资源效率上均优于持续学习基线及其他自进化模型。代码已开源。
原文摘要 · Abstract (English)
Prevailing medical AI operates on an unrealistic ''one-shot'' model, diagnosing from a complete patient file. However, real-world diagnosis is an iterative inquiry where Clinicians sequentially ask questions and order tests to strategically gather information while managing cost and time. To address this, we first propose Med-Inquire, a new benchmark designed to evaluate an agent's ability to perform multi-turn diagnosis. Built upon a dataset of real-world clinical cases, Med-Inquire simulates the diagnostic process by hiding a complete patient file behind specialized Patient and Examination agents. They force the agent to proactively ask questions and order tests to gather information piece by piece. To tackle the challenges posed by Med-Inquire, we then introduce EvoClinician, a self-evolving agent that learns efficient diagnostic strategies at test time. Its core is a ''Diagnose-Grade-Evolve'' loop: an Actor agent attempts a diagnosis; a Process Grader agent performs credit assignment by evaluating each action for both clinical yield and resource efficiency; finally, an Evolver agent uses this feedback to update the Actor's strategy by evolving its prompt and memory. Our experiments show EvoClinician outperforms continual learning baselines and other self-evolving agents like memory agents. The code is available at https://github.com/yf-he/EvoClinician
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