让AI医生自己学会规划诊断策略，更聪明地做医疗决策。
ConfAgents: A Conformal-Guided Multi-Agent Framework for Cost-Efficient Medical Diagnosis
- 通过自演化机制提炼经验，让AI自主优化高阶诊疗策略。
- 在真实临床数据任务上表现超越现有顶尖框架，显著提升诊断效率。
- 适合关注医疗AI自主决策与智能体进化的研究者和开发者。
AI代理在医疗研究中的有效性受限于其对静态预设策略的依赖。这导致一个关键缺陷：代理虽能成为更好的工具使用者，却无法学习成为更优的战略规划者，而这是复杂领域如医疗所必需的能力。我们提出HealthFlow，一种自演化AI代理，通过新颖的元层级演化机制克服这一局限。HealthFlow通过将过程中的成功与失败提炼为持久的战略知识库，自主优化自身高层次问题解决策略。为支撑研究并促进可复现评估，我们引入EHRFlowBench，一个基于同行评审临床研究构建的、包含复杂真实健康数据分析任务的新基准。全面实验表明，HealthFlow的自演化方法显著优于当前最先进的代理框架。本工作标志着从打造更优工具使用者转向设计更智能、自演化的任务管理者，为科学发现中的更自主、高效AI铺平道路。
原文摘要 · Abstract (English)
The efficacy of AI agents in healthcare research is hindered by their reliance on static, predefined strategies. This creates a critical limitation: agents can become better tool-users but cannot learn to become better strategic planners, a crucial skill for complex domains like healthcare. We introduce HealthFlow, a self-evolving AI agent that overcomes this limitation through a novel meta-level evolution mechanism. HealthFlow autonomously refines its own high-level problem-solving policies by distilling procedural successes and failures into a durable, strategic knowledge base. To anchor our research and facilitate reproducible evaluation, we introduce EHRFlowBench, a new benchmark featuring complex, realistic health data analysis tasks derived from peer-reviewed clinical research. Our comprehensive experiments demonstrate that HealthFlow's self-evolving approach significantly outperforms state-of-the-art agent frameworks. This work marks a necessary shift from building better tool-users to designing smarter, self-evolving task-managers, paving the way for more autonomous and effective AI for scientific discovery.
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