发表机构
University of Oxford; Stanford University（牛津大学; 斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MedRSI是首个面向医学的递归自我改进框架，通过临床成本感知的失败优先级排序和快速发现-缓慢注册机制，将诊断失败转化为新临床能力，在多个基准上超越人工设计的医疗智能体。
AI 中文摘要
医疗智能体日益将通用推理模型与专门的临床工具相结合，但其能力在很大程度上仍受限于临床医生和工程师在部署前所设计的内容。递归自我改进（RSI）提供了一种不同的范式，使智能体能够从自身的失败中学习并自主扩展能力，但将RSI直接应用于医学领域会引入根本性的安全挑战。我们提出了MedRSI，这是首个面向医学的递归自我改进框架，它通过工具组合和任务特定模型训练，持续将诊断失败转化为新的临床能力。受临床实践的启发，MedRSI引入了两种机制以实现临床对齐的自我进化。临床成本感知的失败优先级排序根据错误的潜在临床后果而非仅凭频率来引导改进方向。快速发现与缓慢注册将快速的能力发明与保守的采纳相分离，使得新工具只有在后续患者队列中展现出持续获益后才能进入持久智能体。在公共青光眼和心脏病基准以及两项私有临床任务上，MedRSI逐步发展了分割、测量、预测、多模态推理和生成能力，超越了人工设计的医疗智能体，并自主发现了其原始设计者未曾预料到的临床问题解决方案。我们的结果表明，医疗智能体不必受限于部署前指定的能力：通过以临床为基础的管理机制来决定改进什么和保留什么，它们可以持续地从诊断经验中构建、验证和积累新能力。代码可在以下网址获取：https://this URL。
英文摘要
Medical agents increasingly combine general reasoning models with specialized clinical tools, yet their capabilities remain largely fixed by what clinicians and engineers design before deployment. Recursive self-improvement (RSI) offers a different paradigm in which agents learn from their own failures and autonomously expand their capabilities, but directly applying RSI to medicine introduces fundamental safety challenges. We introduce MedRSI, the first recursive self-improvement framework for medicine, which continuously transforms diagnostic failures into new clinical capabilities through tool composition and task-specific model training. Inspired by clinical practice, MedRSI introduces two mechanisms for clinically aligned self-evolution. Clinical-cost-aware failure prioritization directs improvement toward errors according to their potential clinical consequences rather than frequency alone. Fast discovery with slow registration separates rapid capability invention from conservative adoption, allowing new tools to enter the persistent agent only after demonstrating sustained benefit across subsequent patient cohorts. Across public glaucoma and heart disease benchmarks and two private clinical tasks, MedRSI progressively develops segmentation, measurement, prediction, multimodal reasoning, and generative capabilities, surpasses manually engineered medical agents, and autonomously discovers solutions to clinical problems not anticipated by its original designers. Our results show that medical agents need not remain constrained by capabilities specified before deployment: with clinically grounded mechanisms governing what to improve and what to retain, they can continuously construct, validate, and accumulate new capabilities from diagnostic experience. Code is available at https://github.com/ImprintLab/MedRSI.
