Emerging technologies and control systems revolutionized healthcare services which is very evident in self-management of diabetes mellitus by integrating continuous glucose monitors (CGMs), insulin pumps, and hybrid closed-loop systems, which significantly improve glycemic control and reduce hypoglycemia risk. In diabetes management, artificial intelligence (AI) technologies are used for three primary application which are closed-loop control algorithms, glucose prediction through continuous glucose monitoring (CGM) biosensors and AI algorithms, and the calibration of CGM biosensors with the assistance of AI algorithms. Integration of AI technologies into diabetes care supports better clinical outcomes, thereby reducing administrative burden and costs associated with diabetes management. Continuous Glucose Monitoring (CGM) systems, which plays vital role for immediate glucose data delivery, have shown effectiveness in improving diabetes management by reducing HbA1c levels and empowering self-care skills. This has nurtured an increased sense of confidence among patients in managing their medical condition. However, the successful adoption of these technologies requires substantial support from healthcare professionals and family members to ensure adherence and effective use, especially considering factors like family income, educational background, and technological proficiency. In spite of all the research advancements made continuous glucose monitoring (CGM) technologies are still evolving towards compactness, flexibility, sustained functionality, calibration-free operation, and closed-loop systems and free energy harvestability for prolong operation life.
Keywords: Artificial Intelligence, Diabetes Mellitus, biosensors, continuous glucose monitoring, hypoglycemia.
