Diabetes mellitus represents a global metabolic crisis characterized by complex self-management requirements, marked physiological variability, and significant risks of microvascular and macrovascular complications. The integration of Artificial Intelligence (AI)—including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Deep Reinforcement Learning (DRL)—is transforming clinical endocrinology from reactive treatment paradigms into proactive, continuous, and highly personalized care models. This review synthesizes current AI methodologies across diabetes care, examining continuous glucose forecasting, closed-loop automated insulin delivery, point-of-care microvascular screening, population-level risk stratification, and behavioral digital health interventions. Furthermore, it addresses critical translational bottlenecks—such as model opacity, demographic algorithmic bias, and regulatory liabilities—and outlines future paradigms in digital health.
Diabetes mellitus represents a global metabolic crisis characterized by complex self-management requirements, marked physiological variability, and significant risks of microvascular and macrovascular complications. The integration of Artificial Intelligence (AI)—including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Deep Reinforcement Learning (DRL)—is transforming clinical endocrinology from reactive treatment paradigms into proactive, continuous, and highly personalized care models. This review synthesizes current AI methodologies across diabetes care, examining continuous glucose forecasting, closed-loop automated insulin delivery, point-of-care microvascular screening, population-level risk stratification, and behavioral digital health interventions. Furthermore, it addresses critical translational bottlenecks—such as model opacity, demographic algorithmic bias, and regulatory liabilities—and outlines future paradigms in digital health.
