Artificial intelligence for diabetes management – a review

Abstract

Artificial Intelligence (AI) driven algorithms, including machine learning (ML) and deep learning (DL), analyze vast datasets from electronic health records (EHRs), wearable sensors, and continuous glucose monitors (CGMs) to provide accurate predictions and real-time insights. AI applications in diabetes management include automated insulin delivery systems (artificial pancreas), clinical decision support systems (CDSS), dietary and lifestyle coaching, and telemedicine platforms. These innovations improve glycemic control, reduce complications, and empower patients with personalized treatment plans. AI in diabetes care faces challenges such as data privacy concerns, lack of standardization, physician trust issues, and regulatory constraints. Additionally, AI models often suffer from bias due to non-representative datasets, limiting their generalizability across diverse populations. Future advancements will focus on improving AI transparency and explainability, enabling better clinical integration and physician adoption. As AI continues to evolve, its integration into diabetes management holds immense potential to enhance patient outcomes, reduce healthcare burdens, and pave the way for a more efficient, personalized, and data-driven approach to diabetes care.

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