Microbiology and Biotechnology Research: An Overview Vol. 8

Diabetes mellitus represents one of the most significant non-communicable disease burdens of the twenty-first century, affecting approximately 537 million adults globally and projected to reach 783 million by 2045. The increasing prevalence of both type 1 and type 2 diabetes, coupled with the complex, multifaceted nature of disease management, has necessitated a paradigmatic shift in clinical care delivery. Despite decades of advances in pharmacotherapy—including the development of insulin analogues, GLP-1 (Glucagon-like peptide-1) receptor agonists, SGLT-2 (Sodium-glucose co-transporter-2 inhibitor) inhibitors, and DPP-4 (Dipeptidyl peptidase-4 inhibitors) inhibitors—significant gaps persist in achieving optimal glycaemic control at the population level. Artificial intelligence (AI), encompassing machine learning, deep learning, and natural language processing, has emerged as a transformative force with the potential to revolutionise diabetes prevention, diagnosis, monitoring, and treatment. This narrative review examines the breadth of AI applications across the continuum of diabetes care, synthesising evidence from peer-reviewed literature published predominantly between 2010 and 2026. Key areas of focus include AI-driven early detection and risk stratification, automated screening for diabetic retinopathy and other microvascular complications, closed-loop insulin delivery systems, personalised therapeutic decision-making, drug discovery, and AI-enhanced mobile health technologies. The review further addresses critical challenges, including algorithmic bias, data privacy concerns, regulatory frameworks, and barriers to clinical integration. Evidence to date demonstrates that AI-powered tools achieve diagnostic accuracy comparable to, and in many instances exceeding, that of trained clinicians, whilst significantly improving the scalability and accessibility of specialised diabetes care. However, equitable deployment of these technologies remains contingent upon robust validation across diverse populations, transparent governance frameworks, and meaningful clinician–patient engagement. Future directions point towards the convergence of multi-omics data, federated learning architectures, and digital twin modelling as the next frontier in precision diabetes care.