A Systematic Review of Machine Learning and Artificial Intelligence for Diabetes Care

Abstract

Diabetes is a chronic health condition that affects millions of people worldwide, causing significant morbidity and mortality. The prevalence of diabetes is expected to continue rising, making it crucial to develop effective strategies for prevention, early detection, and management. In recent years, machine learning (ML) and artificial intelligence (AI) have emerged as powerful tools in the management of diabetes, with significant applications in automated retinopathy detection, clinical decision support, predictive population risk stratification, and self-management tools for patients. This systematic literature review provides a comprehensive overview of the current state-of-the-art AI and ML-led techniques being used to manage diabetes. The review categorizes and analyzes relevant works in each of the four key areas of diabetes care. It examines the advantages and limitations of using AI and ML in diabetes management and highlights areas where further research is needed. Overall, the review shows that AI and ML have the potential to revolutionize the way diabetes is managed, enabling more accurate and efficient detection, diagnosis, and treatment of the disease. However, the review also points out that further research is needed to address challenges such as data quality, system transparency, and ethical considerations. The review provides valuable insights for researchers, healthcare providers, policymakers, and patients interested in using AI and ML to improve diabetes care.