EvoCare: Towards Efficient and Robust Clinical Diagnosis with Proactive and Interactive Self-Evolving AI Agents

Clinical diagnosis starts with incomplete evidence, so the quality of each question shapes the final decision. Yet successful consultations alone reveal little about which questioning strategies will help future patients. We introduce EvoCare, a self-evolving agent that turns consultation histories into reusable inquiry policies. It contrasts related successes and failures to propose targeted rules, then tests each update on a separate cohort before adoption. Across three clinical diagnosis benchmarks and two language-model backbones, EvoCare outperforms interactive and experience-reuse baselines; on StreamBench-DDxPlus, it improves top-rank pathology accuracy by 2.9-6.3 percentage points over the strongest baseline. Further analyses confirm the contribution of each component and show efficient questioning, low repetition, and consistent performance across models. EvoCare points toward a new generation of clinical AI that learns from experience to navigate diagnostic uncertainty more effectively.