{
  "id": 3540,
  "url": "https://arxiv.org/abs/2605.29368v1",
  "title": "SURGENT: A Surgical Multi-Agent Assistance System Across the Perioperative Workflow",
  "summary": "The intricate nature of modern surgical care necessitates intelligent systems that can synthesize extensive patient records, support collaborative decision-making, and provide transparent, auditable reasoning across the entire perioperative workflow. Although web-based Large Language Models (LLMs) possess advanced reasoning capabilities, they are ill-equipped for surgical applications due to critical limitations: input length constraints, incomplete memory management, and limited traceability. T",
  "authors": "Dongsheng Shi, Yue Li, Xin Yi, Yongyi Cui, Huawei Feng, Linlin Wang",
  "category": "research",
  "topics": "healthcare,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-28T05:12:41.000Z",
  "fetched_at": "2026-07-14T16:30:18.857Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3540",
  "original_url": "https://arxiv.org/abs/2605.29368v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}