ResidencyRL: Reinforcement Learning in Simulated Clinical Environments

Training AI agents for complex, multi-turn clinical reasoning—like a medical resident gaining experience—remains underdeveloped despite LLMs' strong performance on static medical exam benchmarks. ResidencyRL trains clinical AI agents through simulated adversarial patient encounters spanning many dialogue turns and tool calls, rewarding diagnostic accuracy, safety, and communication quality. This has clear applications in clinical decision support, medical training simulators, and diagnostic assistant tools, showing improved diagnostic accuracy and reduced missed red flags versus baseline models, with expert clinicians preferring the trained agent's performance—though real-world prospective validation remains necessary before clinical deployment. Paper: https://arxiv.org/abs/2608.07418