Publications

Artificial intelligence (AI) is transforming diabetes care across prevention, diagnosis, monitoring, and therapy personalization. Machine learning (ML), deep learning, and reinforcement learning approaches, combined with continuous glucose monitoring (CGM), closed-loop insulin delivery systems, electronic health records (EHRs), and multi-omics data, are enabling earlier detection, finer patient stratification, individualized therapeutic choices, and improved glycemic outcomes. Randomized trials and meta-analyses show that AI-enabled systems improve time-in-range (TIR), reduce hypoglycemia, and lessen treatment burden. However, challenges persist regarding data representativeness, model interpretability, regulatory oversight, equity, and clinical workflow integration. This comprehensive review synthesizes the current landscape of AI applications in precision diabetes management, with a focus on predictive modeling, CGM analytics, automated insulin delivery (AID), pharmacotherapy personalization, digital biomarkers, and digital twins. We also discuss implementation challenges, ethical considerations, regulatory frameworks, and future directions to ensure equitable deployment and sustained clinical impact.