Background: Digital health technologies are increasingly applied in diabetes care, enabling continuous monitoring, personalized support and remote interventions. Meanwhile, artificial intelligence (AI) is enhancing the precision and effectiveness of these tools. This study aims to map global research trends and thematic developments in AI-driven digital health technologies for diabetes management and to explore their future directions. Methods: We collected data from the Web of Science Core Collection, including articles and reviews published up to July 12, 2025, using CiteSpace, VOSviewer, and Microsoft Excel to analyze countries/regions, institutions, journals, references, authors, and keywords. Results: A total of 673 publications were included in the analysis. Global publications on AI-driven digital health technologies for diabetes increased steadily, with the USA leading in output. The University of London ranked as the most productive institution. Sensors and diabetes care were the most frequently published and cited journals in this field. Herrero P was among the most prolific authors. The most cited article was “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.” “diabetes” was the most frequently occurring keyword. Keyword cluster analysis identified 3 primary research hotspots: AI-enabled monitoring, digital health interventions, and AI-based diabetic retinopathy screening. Conclusions: This study summarizes the evolution of AI-driven digital health technologies in diabetes care. Although challenges remain in data security, standardization and validation, these technologies hold increasing potential for accurate diagnosis, real-time monitoring and personalized care.

Objective: Digital health technologies are increasingly used in chronic disease management, but their effective use requires adequate digital health literacy (DHL). Because an overall DHL score may mask heterogeneity across its multiple dimensions, this study aimed to identify latent profiles of DHL among young and middle-aged adults with diabetes and to examine factors associated with profile membership, thereby informing individualized health education and stratified health promotion strategies for diabetes management. Methods: Guided by the health ecology model, a cross-sectional study was conducted among 504 hospitalized patients with diabetes aged 18–59 years from two tertiary hospitals in Anhui Province, China, between June 2025 and March 2026. Data were collected using a general information questionnaire, the Digital Health Literacy Instrument, the Diabetes Self-Management Questionnaire, the Technophobia Scale, and the Social Support Rating Scale. Latent profile analysis (LPA) was used to identify DHL profiles. LASSO regression was applied to screen candidate variables, and multinomial logistic regression was used to examine factors associated with profile membership. Results: The mean DHLI score was 2.02 ± 0.61. LPA identified four profiles: limited DHL profile, basic application-low navigation profile, operational strength-limited critical appraisal profile, and high-level comprehensive DHL profile. Multinomial logistic regression showed that age, educational level, residence, complications, mobile health tool use, daily internet use, online health information seeking, diversity of information sources, technophobia, and social support were significantly associated with DHL profile membership ( p < 0.05). Conclusion: DHL among young and middle-aged adults with diabetes showed distinct profile characteristics in information access, appraisal, interaction, and privacy protection. These findings provide a basis for individualized health education and stratified health promotion strategies based on patients’ DHL profiles in diabetes management.