Abstract
Prediabetes represents an early stage of glucose metabolism disorder with significant public health implications. Although traditional lifestyle interventions have demonstrated some efficacy in preventing the progression to type 2 diabetes, their limitations-such as lack of personalization, restricted real-time monitoring, and delayed intervention-are increasingly apparent. This article systematically explores the potential applications of continuous glucose monitoring (CGM) technology combined with artificial intelligence (AI) in the management of prediabetes. CGM provides real-time and dynamic glucose monitoring, addressing the shortcomings of conventional methods, while AI enhances the clinical utility of CGM data through deep learning and advanced data analysis. This review examines the advantages of integrating CGM and AI from three perspectives: precise diagnosis, personalized intervention, and decision support. Additionally, it highlights the unique roles of this integration in remote monitoring, shared decision-making, and patient empowerment. The article further discusses challenges related to data management, algorithm optimization, ethical considerations, and future directions for this technological integration. It proposes fostering multidisciplinary collaboration to promote the application of these innovations in diabetes management, aiming to deliver a more precise and efficient health management model for individuals with prediabetes.