XGBoost reduced incident T2DM by 18% and 0.5% HbA1c in a Han Chinese cohort, DL models improved TIR by 15% without increasing hypoglycemia, and digital twins decreased severe hypoglycemia by 35% in T2DM patients.
AI and digital health tools offer significant potential for precise prediction and personalized management of T2DM, but equitable global application requires addressing algorithmic bias, data privacy, and the digital divide in LMICs.
Effect estimate: XGBoost model reduced incident T2DM by 18% with 0.5% HbA1c reduction; DL models increased TIR by 15% without hypoglycemia increase; Federated learning reduced HbA1c by 0.7%; Digital twins reduced severe hypoglycemia by 35%; Edge AI reduced diagnostic delays by 40%
Type 2 diabetes mellitus (T2DM) poses a significant global public health challenge, with its prevalence escalating continuously and disproportionately affecting low- and middle-income countries (LMICs), imposing a substantial burden on healthcare systems. Traditional management models have limitations in disease prediction, personalized treatment, and public health intervention. Artificial intelligence (AI) and digital health technologies provide novel insights for precise prediction and intelligent management of T2DM. This review systematically summarizes research progress in AI’s role in deciphering T2DM pathogenesis, personalized treatment, and public health management. By integrating multi-omics and environmental data, AI reveals key mechanisms including gene–environment (G × E) interactions, β -cell dysfunction, and inflammatory pathways, significantly enhancing early screening and risk prediction. In clinical management, AI combined with digital health tools e.g., continuous glucose monitoring (CGM), wearable devices, and mobile health (mHealth) apps facilitates remote monitoring, medication optimization, and personalized interventions, improving treatment adherence and health management efficiency. At the public health level, AI optimizes resource allocation and disease burden assessment, promoting chronic disease prevention and control model transformation. Future efforts should prioritize developing low-resource-adapted tools, strengthening data privacy protection tailored to LMICs, and addressing algorithmic fairness and the digital divide to ensure safe, equitable, and sustainable AI application in global T2DM management. Overall, AI and digital health integration is driving T2DM management towards an intelligent and precision-based era, with the potential to reduce disparities in LMICs.
Chonger Yu (2026) conducted a review in Adults with type 2 diabetes mellitus (T2DM) in low- and middle-income countries and diverse populations at risk of T2DM. AI-based prediction models including XGBoost, deep learning (DL), federated learning (FL), digital twins, edge AI vs. Traditional statistical methods or usual care was evaluated on 5-year incidence of type 2 diabetes mellitus or reduction in HbA1c and time-in-range (TIR) (XGBoost model reduced incident T2DM by 18% with 0.5% HbA1c reduction; DL models increased TIR by 15% without hypoglycemia increase; Federated learning reduced HbA1c by 0.7%; Digital twins reduced severe hypoglycemia by 35%; Edge AI reduced diagnostic delays by 40%). XGBoost reduced incident T2DM by 18% and 0.5% HbA1c in a Han Chinese cohort, DL models improved TIR by 15% without increasing hypoglycemia, and digital twins decreased severe hypoglycemia by 35% in T2DM patients.