Clinical Report: Generative AI Use for Health Information Seeking Among Adults With Diabetes or Prediabetes

Overview

The article describes a study of generative AI (GenAI) health-information seeking among adults with diabetes or prediabetes and examines whether general GenAI use is associated with diabetes-specific seeking. Its stated framework evaluates perceived utility, critical literacy, verification behavior, behavioral impact, and trust/risk awareness; the supplied material does not include study results.

Background

Diabetes management involves ongoing decisions about glucose indicators, medications, diet, physical activity, and when to seek professional advice, many of which arise outside scheduled visits. GenAI can provide conversational answers that may help users understand medical terminology and prepare questions for healthcare professionals. However, AI-generated health information may be inaccurate, incomplete, outdated, or insufficiently individualized for diabetes-management decisions. The study therefore focuses on diabetes-specific GenAI information seeking and on users’ assessment and use of AI-generated information.

Data Highlights

Key Findings

• The study included 617 adults with diabetes or prediabetes, aged 19–92 years.
• Type 2 diabetes was the most frequently reported condition (422 participants; 68.4%), followed by type 1 diabetes (100; 16.2%) and prediabetes (66; 10.7%).
• H1 asks whether general GenAI health-information use is associated with diabetes-specific GenAI information seeking.
• H2 proposes positive associations between general GenAI health-information use and perceived utility, critical literacy, verification behavior, behavioral impact, and trust/risk awareness.
• H3 proposes positive associations between diabetes-specific seeking and perceived utility, critical literacy, verification behavior, and behavioral impact; it proposes an association with trust/risk awareness.
• H4 proposes indirect effects through the five dimensions. The supplied material describes the hypotheses and sample but does not report results for these associations or indirect effects.

Clinical Implications

The article identifies GenAI as a potential source of diabetes-related information while noting concerns about accuracy, completeness, currency, and individualization. The supplied material does not report findings that establish how participants used or evaluated GenAI, so it does not support specific practice recommendations.

Conclusion

This study frames diabetes-specific GenAI information seeking as a question involving both general use and users’ perceptions and behaviors. Results are not included in the provided source material.

Related Resources & Content

1. Article source, Generative AI Use for Health Information Seeking Among Adults With Diabetes or Prediabetes, publication details not provided
2. Digital Health, Quality evaluation of AI-generated diabetes-related health education texts from different generative models, year not provided
3. Digital Health, Patients Empowered by Artificial Intelligence, year not provided
4. Journal of Medical Internet Research, Exploring the Role of Generative AI in Health Information Retrieval: A Scoping Review of Enablers and Challenges for Consumers and Patients, 2026
5. Journal of Medical Internet Research (JMIR) — Trusting Generative AI for Health Advice: Preregistered Survey Experiment
6. National Diabetes Statistics Report - United States Diabetes Surveillance System
7. Diabetes Technology: Standards of Care in Diabetes—2026
8. Assessing the information quality of AI-generated patient educational materials for diabetes: a scoping review.