acePrevalent knowledge omissions: An unseen risk of LLMs in patient-friendly adaptation of medical literature
Author summary Many patients look to medical research articles to better understand their conditions and treatment options. Recently, large language models (LLMs) have been used to rewrite complex medical texts into more accessible language. While this approach may improve understanding, it also raises concerns about whether important medical knowledge is preserved. In this study, we introduce a method called Knowledge Triplet Comparison (KTC) to measure how medical knowledge changes when texts are rewritten for…
When six widely used LLMs simplified or shortened medical texts for patients, on average only about half of the original medical knowledge was retained, and some unstated knowledge was added.
The evaluation used chemical-disease relationship data, so the findings are grounded in that specific kind of medical knowledge and may not cover every type of medical content.
Evidence
- JournalismPLOS (Public Library of Science)2026-10-15
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Truvace Impact Record TRV-2026-1499, v1: “Prevalent knowledge omissions: An unseen risk of LLMs in patient-friendly adaptation of medical literature.” Truvace, 2026-10-15. /record/TRV-2026-1499 (accessed at citation time). sha256 443319cee4a83f56…
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