Assessing the information quality of AI-generated patient educational materials for diabetes: a scoping review
Abstract
Objective: To map the evidence on artificial intelligence (AI)-generated diabetes-related patient education materials and patient-facing health information, with particular attention to AI models, prompting approaches, evaluation methods, and information-quality outcomes.
Methods: This scoping review was conducted in accordance with the JBI methodology for scoping reviews and reported following the PRISMA-ScR checklist. The review was registered on the Open Science Framework (doi: 10.17605/OSF.IO/U4FAE) PubMed, Web of Science, Embase, Scopus, Cochrane CENTRAL, CNKI, WanFang Data, and SinoMed were searched from inception to May 1, 2026. Chinese- and English-language literature was searched. Two reviewers independently screened studies, charted data, and mapped reported outcomes to Wang and Strong's information quality framework. Outcomes not adequately represented by the framework were retained as additional dimensions. Descriptive statistics and narrative synthesis were used.
Results: Of 6,049 records identified, 24 studies from 11 countries or regions were included. All studies evaluated ChatGPT or another GPT-family model; 21 used zero-shot or direct prompting, three used role prompting, and two implemented retrieval-augmented generation. Eleven indicators were mapped to the information quality framework, with ease of understanding (n = 14), accuracy (n = 13), and believability (n = 9) assessed most frequently. Six additional outcomes were identified: clinical safety (n =…
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