Artificial intelligence in diabetes: Clinical applications across the disease continuum

Artificial intelligence (AI) is increasingly shaping the landscape of diabetes mellitus (DM) care by enabling data-driven approaches to prediction, diagnosis, classification, and management. The growing availability of continuous biometric data from wearable technologies, combined with advances in machine learning, has created new opportunities to overcome limitations of traditional diabetes metrics and population-based care models. This review summarises current clinical applications of AI in DM, with a focus on translational relevance and, critically, on the level of validation and clinical readiness of each application. AI-based models have demonstrated improved performance in predicting DM onset and diabetes-related complications by integrating electronic health records, imaging data, and longitudinal clinical variables. In diagnosis and screening, AI offers non-invasive and scalable tools, including automated retinal screening systems and emerging digital biomarkers. Data-driven clustering approaches have further refined diabetes classification. In diabetes management, AI supports personalised glucose monitoring, insulin titration, pharmacological decision-making, and lifestyle interventions. Crucially, these applications differ markedly in maturity: autonomous retinal screening and closed-loop insulin delivery are approaching or have reached routine clinical use, whereas voice-based biomarkers and simulation-evaluated lifestyle systems remain proof-of-concept. Addressing challenges of generalisability, data quality, interpretability, and ethical governance through robust validation and explainable AI will be essential to realise the full potential of AI in precision, patient-centred diabetes care.