ArticleFrontiers in digital health2026
AI-driven personalised management platform enhances glycaemic control and remission rates in type 2 diabetes: a quasi-experimental prospective cohort study.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background and aim: Although artificial intelligence (AI) technologies show promise in diabetes management, real-world evidence supporting AI-driven personalized care remains limited. We developed an AI-powered platform that generates dynamic self-management plans (DSMPs) and enables continuous, whole-process patient monitoring. This study aimed to evaluate its effectiveness in patients with type 2 diabetes mellitus (T2DM). Design: This was a 12-month, multicenter, prospective, open-label, quasi-experimental cohort study. A total of 1,452 eligible patients with T2DM were enrolled between April 2022 and April 2023 and assigned by an AI-driven platform, based on enrollment sequence, to either the intervention group ( Findings: Among the 1,343 patients who completed the study (655 in the intervention group and 688 in the control group), the intervention group demonstrated significantly better glycemic control at 3 months. Reductions in glycated hemoglobin (HbA1c) (1.92% vs. 1.31%) and fasting plasma glucose (FPG) (3.23 vs. 2.31 mmol/L) were significantly greater in the intervention group, and significantly more patients achieved treatment targets for both HbA1c (57% vs. 26%) and FPG (68% vs. 39%). Furthermore, the intervention led to significant improvements (all Conclusions: AI-driven personalized management was feasible and effective for the management of T2DM and was associated with an increased rate of diabetes remission. However, further evidence is required to justify its integration into routine clinical practice, and improvements in cholesterol management remain necessary.
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