UroMed-Agents: An Interactive Multi-Agent Framework for Clinical Decision-Making Training in Urology

Effective and standardized clinical decision-making (CDM) training is essential for preparing clinicians to diagnose and treat urological diseases. However, traditional approaches are constrained by limited access to longitudinal cases and expert supervision, making it difficult to cover traceable decision-making processes. Recent advances in large language models (LLMs) enable interactive medical environments, but most methods either evaluate autonomous artificial intelligence (AI) clinicians or focus on isolated history taking tasks, providing limited support for end-to-end, multi-turn training of human clinicians. To address this, we propose UroMed-Agents, an interactive multi-agent framework for CDM training in urology. The framework centers on human clinicians and employs four agents for patient interaction, examination result delivery, process guidance, and final evaluation. To reduce role overreach and information leakage across turns, we introduce a stateful Case Engine that maintains interaction state, enforces information boundaries for each role, and releases examination results only when requested. Furthermore, the framework separates training and testing scenarios and evaluates clinician performance based on interaction records and final reports, while diagnostic performance at the system level is assessed using metrics based on the International Classification of Diseases (ICD-10). Experimental results on 239 real longitudinal urology cases illustrate the effectiveness of our method, which achieves better performance than existing interactive approaches.