{
  "id": 3601,
  "url": "https://arxiv.org/abs/2605.28338v1",
  "title": "SafeMed-R1: Clinician-Audited Safety and Ethics Alignment for Medical Large Language Models",
  "summary": "Large language models(LLMs) increasingly match expert performance on licensing examinations, yet routine clinical use remains limited because governance requires auditable reasoning, safety and ethics alignment, and resilience to adversarial misuse. Here we present SafeMed-R1, trained with a traceable Clinical Trust Signals(CTS) pipeline that links each reasoning instance to clinician rubric scores and edit histories, and aligned through safety and ethics supervision and red team stress testing.",
  "authors": "Chao Ding, Mouxiao Bian, Tianbin Li, Minjia Yuan, Yidong Jiang, Yankai Jiang et al.",
  "category": "research",
  "topics": "regulation,safety-alignment,copyright-ip,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-27T11:42:52.000Z",
  "fetched_at": "2026-07-14T16:30:23.245Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3601",
  "original_url": "https://arxiv.org/abs/2605.28338v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}