TransplantQA corpus¶
Product or Programme · All products and programmes
Evidence · 3 claims (3 for) · 1 study (1 associational),q2· 0 of 1 report an effect size · 3 claims rest on one study
Description¶
A corpus of 102 U.S. transplant patient-education handbooks paired with 1,115 organ- and topic-labelled patient-derived questions, created by Yubo Li et al. to audit retrieval-augmented patient-facing AI.
Components¶
Claims¶
- Reproductive health shows the highest coverage gap (82.1% absence) and the highest judge-rated significance among divergent pairs (86%), making it a priority content area [+W]
- Same-center cross-organ handbook agreement exceeds different-center same-organ agreement by 0.024, but the institution contrast is sensitive to coverage thresholds and document selection [+W]
- Question and observed-coverage features retrospectively predict high-divergence questions with AUC 0.774, but the model is not validated for new-query routing [+W]
Related Products and Programmes¶
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Key Sources¶
- Yubo Li, Rema Padman, Ramayya Krishnan. (2026). Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US Transplant Handbooks. arXiv preprint. https://arxiv.org/abs/2607.22606
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
transplantqa-corpus |
title |
TransplantQA corpus |
description |
A corpus of 102 U.S. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
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