aceQuality and readability of large language model–generated responses on type 2 diabetes remission: a cross-sectional balanced factorial study
Abstract Large language models (LLMs) are increasingly being explored for patient education and medical information delivery. However, their outputs may involve trade-offs among medical accuracy, comprehensiveness, empathy, and readability. Type 2 diabetes remission is a clinically nuanced concept that is readily misunderstood by patients, making it a suitable context in which to evaluate both the medical quality and patient accessibility of artificial intelligence–generated health information. To systematically…
Chatbot answers about type 2 diabetes remission could not be optimized on every measure at once, and the easiest-to-read answers scored lower on comprehensiveness and falsehood avoidance.
The correlation analysis between readability and quality did not account for clustering within question and was intended for hypothesis generation only, and findings apply only to the type 2 diabetes remission question set.
Evidence
- Peer-reviewedScientific Reports2026-10-29
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Truvace Impact Record TRV-2026-2315, v1: “Quality and readability of large language model–generated responses on type 2 diabetes remission: a cross-sectional balanced factorial study.” Truvace, 2026-10-30. /record/TRV-2026-2315 (accessed at citation time). sha256 61b04d8b26a4c0c1…
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