{
  "id": 5156,
  "url": "https://arxiv.org/abs/2604.28010v2",
  "title": "Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care",
  "summary": "We reframe clinician overrides of clinical AI recommendations as implicit preference data - the same signal structure exploited by reinforcement learning from human feedback (RLHF), but richer: the annotator is a domain expert, the alternatives carry real consequences, and downstream outcomes are observable. We present a formal framework extending standard preference learning with three contributions: a five-category override taxonomy mapping override types to distinct model update targets; a pr",
  "authors": "Prabhjot Singh, Abhishek Gupta, Chris Betz, Abe Flansburg, Brett Ives, Sudeep Lama et al.",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-30T15:30:47.000Z",
  "fetched_at": "2026-07-14T16:31:31.213Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5156",
  "original_url": "https://arxiv.org/abs/2604.28010v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}