ResidencyRL: Reinforcement Learning in Simulated Clinical Environments

In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of feedback and progressively greater autonomy. Much of clinical reasoning relies on the patient encounter, a dialogue in which a clinician elicits history, refines diagnostic hypotheses, and decides management under uncertainty. While large language models (LLMs) excel on static medical benchmarks, methods to optimize the f

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ethics.ai (7 August 2026), “ResidencyRL: Reinforcement Learning in Simulated Clinical Environments,” evidence record 17796, https://ethics.ai/record/17796 (originally published by arXiv).

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