{
  "id": 13781,
  "url": "https://arxiv.org/abs/2607.22606",
  "title": "Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US Transplant Handbooks",
  "summary": "arXiv:2607.22606v1 Announce Type: new Abstract: Health systems are rapidly deploying generative AI assistants that answer patient questions from institution-authored education materials, on the premise that grounding in local content yields consistent guidance. Whether it does depends on a question not previously measured at scale: do the underlying documents themselves agree? We use a structured-output large language model judge to audit 5,730,465 pairwise comparisons across 102 patient-educati",
  "authors": "Yubo Li, Rema Padman, Ramayya Krishnan",
  "category": "research",
  "topics": "healthcare,children-education,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-28T04:00:00.000Z",
  "fetched_at": "2026-07-28T05:10:12.325Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13781",
  "original_url": "https://arxiv.org/abs/2607.22606",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}