用自进化智能体提升罕见眼病诊断准确率
Evo-RAD: Navigating Rare Retinal Disease Diagnosis via Self-Evolving Agentic Retrieval
- 将检索过程建模为马尔可夫决策，动态优化证据集
- 在罕见眼病数据集上提升诊断准确率21.04%
- 适合需要高精度罕见病诊断的临床研究场景
大规模预训练基础模型虽推动了通用医疗筛查，但在罕见疾病诊断中表现不佳，因真实临床数据中此类病例稀少。现有检索增强方法常受“中心性问题”困扰，检索到的是视觉相似但语义错误的常见病。为此，我们提出Evo-RAD，一种自进化智能体框架，将证据获取转化为动态决策任务。将检索建模为马尔可夫决策过程（MDP），基于图的智能体观察参考集状态，执行删除不一致证据（DELETE）、插入病理性一致样本（INSERT）或终止演化（TERMINATE）等动作。通过具有同质性感知奖励的组相对策略优化（GRPO）进行训练，智能体学习最大化支持参考集的诊断同质性。在视网膜疾病基准测试中，Evo-RAD显著提升罕见病诊断性能，相较视网膜基础模型提升+21.04%，优于检索增强与参数高效微调方法+3.56%。代码已开源。
原文摘要 · Abstract (English)
Large-scale pretrained foundation models have revolutionized general medical screening, but often falter on rare diseases because such conditions are underrepresented in real-world clinical datasets. While retrieval-augmented diagnosis attempts to mitigate this, conventional static methods frequently succumb to the hubness problem, retrieving visually similar but semantically incorrect common diseases. To address this, we propose Evo-RAD, a self-evolving agentic framework that transforms evidence acquisition into a dynamic decision-making task. We formulate retrieval as a Markov Decision Process (MDP) where a graphbased agent observes the reference set state and executes actions to purge discordant evidence (DELETE), acquire pathologically consistent samples (INSERT), or conclude the evolution (TERMINATE). Optimized via Group Relative Policy Optimization (GRPO) with a homogeneityaware reward, the agent learns to maximize the diagnostic homogeneity of the support reference set. Experiments on retinal disease benchmarks show that Evo-RAD substantially improves rare-disease diagnosis, outperforming retinal foundation models by +21.04%, while also surpassing retrieval-based and parameter-efficient fine-tuning methods by +3.56%. Code is available at https://github.com/SDH-Lab/Evo-RAD.
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