{
  "id": 18780,
  "url": "https://arxiv.org/abs/2608.12025v1",
  "title": "From Safety Documentation to Safety Knowledge Support: An Evidence-Grounded LLM Framework for Medical Devices",
  "summary": "Medical devices are becoming more software-intensive, connected, and AI-enabled. Their development requires risk-management evidence aligned with ISO 14971 and, for software, IEC 62304. This evidence must be kept consistent across requirements, design decisions, software changes, verification results, complaints, and post-market data. These tasks are costly and depend on scarce safety and domain experts. Large language models (LLMs) may reduce parts of this effort because medical-device safety w",
  "authors": "Tuhinangshu Gangopadhyay, Rasmus Adler, Peter Liggesmeyer, Jan Reich",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T13:05:49.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18780",
  "original_url": "https://arxiv.org/abs/2608.12025v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}