DiaBench: A Benchmarking Framework to Evaluate the Performance of LLMs in T2D Patient Education

Background Effective type 2 diabetes (T2D) education improves patient outcomes, but clinician time constraints limit what can be covered during encounters. Large language models (LLMs) offer a scalable way to answer patient questions, yet their accuracy and reliability in T2D education across health literacy levels remain poorly characterized. Methods We developed DiaBench, a standardized benchmarking framework built from 96 question-and-answer pairs collected by clinicians from representative real-world patient encounters. GPT-5 transformed each pair into multiple-choice questions (MCQs) unde

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