Evaluation of large language model-generated information in diabetes health patient education: a scoping review.

Shiya Hu, Binfen Lin, Fenglan Lin

Journal: Frontiers in public health 2026;14():1850486

PMID: 42415766

Abstract

BACKGROUND

Diabetes is a leading cause of disability and death, posing a heavy healthcare burden globally. While standardized health education is crucial for glycemic control and mitigation of complications, traditional educational models face challenges due to insufficient scalability. The ongoing development of AI-based large language model (LLM) methods and technologies presents significant opportunities for health education in the field of diabetes.

OBJECTIVE

A scoping review of research on LLM-generated information for diabetes patient health education: Synthesizing current application status and performance outcomes.

METHODS

The Joanna Briggs Institute (JBI) evidence-based healthcare centre's scoping review guidance was utilized as the methodological framework, then five databases (PubMed, Embase, Web of Science, (American Psychological Association) APA PsycNet, and The Cochrane Library) were searched to retrieve studies from their inception to March 26, 2026. Two reviewers independently performed literature screening, full-text reading, and data extraction.

RESULTS

A total of 21 studies from nine countries were included. Application scenarios were categorized into five domains: general health education, dietary education, complication education, exercise education and technology education. Overall, the existing evidence indicates that LLMs perform well in terms of accuracy and completeness; however, significant limitations remain in readability, reliability, and usability. Moreover, ethical and safety concerns are prominent, including data security, fairness, patient safety, and liability.

CONCLUSION

Despite existing technical and ethical challenges, LLMs still have potential as an auxiliary tool in diabetes health education. Future research needs to enhance technical design optimization, develop patient-centered designs, standardize evaluation metrics, and structured ethical oversight to further validate their practical application effects in diabetes health education for patients with diabetes.

Address: Department of Education, The Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.; School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.; Department of Respiratory and Critical Care Medicine, Lishui Central Hospital, Lishui, Zhejiang, China.

DOI: 10.3389/fpubh.2026.1850486

MeSH Terms: Humans, Large Language Models, Patient Education as Topic, Diabetes Mellitus