The application of AI-based interventions in diabetes personalized management: a systematic review and meta-analysis

Abstract

Artificial intelligence (AI)–based interventions are increasingly integrated into diabetes management, but the certainty of their clinical benefits remains uncertain. We conducted a systematic review and meta-analysis of 13 studies assessing AI-based interventions for personalized diabetes care. Primary outcomes included glycated hemoglobin (HbA1c), fasting blood glucose (FBG), body mass index (BMI), lipid profiles, and self-management capacity. The certainty of evidence was assessed using the GRADE framework and compared with a recent scoping review of 45 studies published in the Journal of Medical Internet Research. Meta-analysis showed a significant reduction in HbA1c (MD − 0.35%, 95% CI − 0.60 to − 0.10; GRADE: moderate). For FBG, BMI, and lipid outcomes, effect estimates were imprecise and graded low to very low. Narrative findings suggested improvements in self-management and quality of life. Comparison with the scoping review confirmed consistent benefits in HbA1c reduction and self-management across digital tools; however, both reviews highlighted limitations, including small sample sizes, short follow-up periods, and heterogeneity. AI-based interventions hold promise for enhancing glycemic control and patient self-management in diabetes care; however, current evidence is limited. Future large, multicenter randomized controlled trials with more extended follow-up periods and cost-effectiveness analyses are needed to establish the long-term effectiveness and sustainability of this approach.

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