Blood Glucose Monitoring in Type 1 Diabetes: Advances in CGM Technology, Automated Insulin Delivery and AI-Driven Care

Continuous glucose monitoring (CGM) has transformed the management of type 1 diabetes by providing real-time and retrospective data that support more precise and timely therapeutic decisions. This chapter offers a comprehensive examination of contemporary CGM technologies, their demonstrated clinical benefits, and the key challenges that continue to influence their implementation, including sensor accuracy, physiological and technological constraints, skin-related adverse reactions, and interference from medications and other substances. As CGM systems have become increasingly integrated into automated insulin delivery (AID) platforms, the advantages and limitations of hybrid closed-loop systems are analyzed alongside essential safety, regulatory, and cybersecurity considerations. The chapter also highlights the growing importance of interoperability, with particular focus on emerging strategies for incorporating CGM data into electronic health record (EHR) systems to enhance remote monitoring, clinical decision-making, and workflow efficiency. In addition, the expanding role of artificial intelligence (AI) in next-generation diabetes technologies is explored. Advances in machine learning, multimodal physiological sensing, and edge computing are enabling more adaptive, personalized, and autonomous glucose regulation, accelerating progress toward the long-term vision of a fully automated artificial pancreas. Together, these developments illustrate a rapidly evolving technological landscape in which CGM, AID, EHR integration, and AI converge to improve glycemic outcomes, reduce treatment burden, and enhance the quality of care for individuals living with type 1 diabetes.