Artificial intelligence and medical-engineering integration in diabetes management: Advances, opportunities, and challenges

Global incidence and prevalence of diabetes have been rising steadily, leading to increased disability, mortality, and a significant economic burden. Despite advances in medical care, challenges such as shortage of diabetes specialists, unequal distribution of healthcare resources, suboptimal medication adherence, and poor self-management have contributed to inadequate glycemic control in patients. This review examines the latest advances in artificial intelligence (AI) applications for diabetes management, evaluating its potential to improve patient outcomes and reduce the economic burden on healthcare systems. We comprehensively reviewed recent studies and clinical trials that explore the use of AI in diabetes prevention, diagnosis, and management. Key technologies such as machine learning, predictive analytics, and digital health tools were assessed for their clinical applicability and impact on patient care. AI-driven approaches, including predictive models for glycemic control, personalized treatment plans, and digital monitoring systems, have shown promising results in enhancing diabetes management. However, challenges remain in integrating these technologies into clinical practice, particularly regarding data privacy, algorithmic transparency, and training of healthcare providers. While AI presents substantial opportunities for improving diabetes care and reducing healthcare costs, its successful implementation requires overcoming several barriers, including regulatory hurdles and ensuring equitable access to technology. Future research should focus on developing interoperable AI systems that seamlessly integrate into existing healthcare infrastructures and address the diverse needs of diabetic populations.