Diabetes mellitus is a rapidly growing global health challenge, with over 589 million affected individuals and a projected prevalence exceeding 853 million by 2050. The complexity of its management, involving continuous monitoring, multifactorial interventions, and long-term complication prevention, has highlighted the need for innovative approaches. Artificial intelligence (AI), encompassing machine learning (ML), deep learning (DL), and natural language processing (NLP), is increasingly applied in diverse domains of diabetes care. AI supports early diagnosis, individualized treatment, prediction of complications, and patient engagement through digital health platforms. Moreover, AI-driven closed-loop insulin delivery systems represent a paradigm shift toward personalized, automated glucose regulation. This narrative review synthesizes the current evidence regarding the applications, benefits, and limitations of AI in diabetes management. Emphasis is given to diagnostic prediction models, glycemic control, complication surveillance, digital coaching, and integration into healthcare systems. Challenges such as data heterogeneity, algorithmic bias, and regulatory frameworks are critically analyzed. Future perspectives include federated learning, multimodal precision diabetology, and ethical frameworks for equitable implementation. The integration of AI into clinical practice offers transformative potential, but requires rigorous validation, patient-centered design, and interdisciplinary collaboration.
Varrassi et al. (Mon,) studied this question.