Effectiveness of AI-driven interventions in glycemic control: A systematic review and meta-analysis of randomized controlled trials.

This systematic review aims to assess the effectiveness of AI-Driven Decision Support Systems in improving glycemic control, measured by Time in Range (TIR) and HbA1c levels, in patients with diabetes. Included studies were randomized controlled trials (RCTs) that evaluated AI interventions in diabetes management. Exclusion criteria included non-English studies, non-peer-reviewed articles. Studies were identified by searching electronic databases including PubMed, EMBASE, and Cochrane Library up to December 2024. Risk of bias was assessed using the Cochrane Risk of Bias tool for RCTs. Results were synthesized using a random-effects meta-analysis model. The review included 17 RCTs with a total of 3381 participants in the intervention group and 3176 in the control group. AI interventions were found to significantly improve TIR and reduce HbA1c levels. The meta-analysis for TIR yielded a mean difference of 0.54 (95 % CI: 0.05-1.03), and for HbA1c a standardized mean difference of -0.91 (95 % CI: -1.23 to -0.58). Evidence was limited by high heterogeneity (I² > 90 % for both outcomes) and indications of publication bias, which may overestimate the effectiveness reported. Despite limitations, the results support the potential of AI interventions in enhancing diabetes management, though variability in effectiveness suggests the need for personalized approaches.