ResidencyRL: Reinforcement Learning in Simulated Clinical Environments
The paper trains clinical AI agents with reinforcement learning across simulated, multi-turn patient encounters.
ResidencyRL uses LLM patient simulators, including adversarial cases, and rewards diagnostic accuracy, management, communication, documentation, and safety. In held-out tests, the trained agent improved diagnostic accuracy under adversarial conditions from 81.0% to 88.0% and cut missed red flags by 31%. Blinded clinicians preferred it in 87.6% of side-by-side comparisons. The authors say real-world workflow validation is still needed before clinical utility can be established. ArXiv · AI/CL/LG's note
ResidencyRL uses LLM patient simulators, including adversarial cases, and rewards diagnostic accuracy, management, communication, documentation, and safety. In held-out tests, the trained agent improved diagnostic accuracy under adversarial conditions from 81.0% to 88.0% and cut missed red flags by 31%. Blinded clinicians preferred it in 87.6% of side-by-side comparisons. The authors say real-world workflow validation is still needed before clinical utility can be established. ArXiv · AI/CL/LG's note
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