Abstract

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.