Large-Scale Audit Reveals Institutional Disagreement in Patient Education Materials for Generative AI
A large-scale study used a structured-output language model to compare 102 patient-education handbooks from 23 US transplant centers, conducting over 5.7 million pairwise comparisons. The analysis found that handbooks from the same institution agreed more with each other across different organ types than handbooks for the same organ from different centers. Notably, reproductive health topics were both frequently missing and, when present, showed the highest rates of clinically significant disagreement. These findings highlight substantial inconsistencies in the source materials used to ground generative AI for patient education.
Why it matters: The study demonstrates that relying on institution-authored materials for AI-generated patient guidance may not ensure consistency or safety, raising important concerns for healthcare AI deployment.
Full story at: arXiv Computers and Society ↗