{
  "id": 17796,
  "url": "https://arxiv.org/abs/2608.07418v1",
  "title": "ResidencyRL: Reinforcement Learning in Simulated Clinical Environments",
  "summary": "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",
  "authors": "Valentin Liévin, Samuel Schmidgall, Tim Strother, Alex Bijamov, Akshay Goel, Anil Palepu et al.",
  "category": "research",
  "topics": "healthcare,children-education,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T17:04:41.000Z",
  "fetched_at": "2026-08-10T05:10:00.488Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/17796",
  "original_url": "https://arxiv.org/abs/2608.07418v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}