AI in Continuous Blood Glucose Monitoring Systems

Abstract

Continuous Blood Glucose Monitoring (CGM) systems have revolutionized diabetes management by providing realtimeinsights into glucose fluctuations, enabling patients and healthcare providers to take proactive measures. The integration ofArtificial Intelligence (AI) into CGM systems has significantly enhanced their efficiency, accuracy, and predictive capabilities.AI algorithms analyze complex and voluminous glucose data to identify patterns, predict future trends, and offer personalizedrecommendations. This paper explores the applications of AI in CGM, examining how machine learning and deep learningmodels are being used for improved glycemic control, early detection of glucose anomalies, behavior prediction, and adaptiveinsulin therapy. It also discusses the impact of AI-driven CGMs on patient engagement, remote monitoring, and clinical decisionmaking.Ethical concerns, data privacy, and technological limitations are also addressed. This comprehensive analysisunderscores AI’s transformative role in reshaping diabetes care, making it more precise, predictive, and patient-centric.