Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US Transplant Handbooks
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
Record details
Published: 28 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Healthcare · Children & education · Transparency
Retrieved: 28 July 2026
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ethics.ai (28 July 2026), “Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US Transplant Handbooks,” evidence record 13781, https://ethics.ai/record/13781 (originally published by arXiv cs.CY).
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