AI-Enhanced Wearable Glucose Sensors: From Intelligent Sensing to Closed-Loop Management of Diabetes

Wearable glucose sensors enable continuous monitoring of glycemic dynamics, yet their clinical utility is constrained by signal instability, physiological delay, and limited predictive capability. Artificial intelligence (AI) has been increasingly integrated to address these limitations. This review summarizes recent advances in AI-enhanced wearable glucose sensors, with a focus on the evolution of sensing technologies and the role of AI across three main aspects: calibration, glucose prediction, and wearable closed-loop control. In calibration, AI-based approaches for improving short-term signal reliability and compensating long-term drift are reviewed. In prediction, methods ranging from continuous glucose monitoring (CGM)-based forecasting to multimodal sensor fusion and noninvasive sensing are summarized, including both continuous glucose estimation and event-oriented detection of hypo- and hyperglycemia. The integration of sensing, prediction, and insulin delivery in wearable closed-loop artificial pancreas is also discussed. Finally, the remaining challenges and future directions are discussed, highlighting the potential of AI-enhanced wearable glucose sensors to support more reliable and integrated diabetes management.