Digital twin paradigm in diabetes prediction and management

Traditional diabetes management employs reactive strategies with therapeutic adjustments after adverse glycaemic events rather than proactive prevention, resulting in suboptimal control and increased complications. Digital twin (DT) technology creates virtual replicas through computational modelling and real time data integration as a transformative approach. However, questions remain regarding clinical validation, implementation feasibility, and generalisability. This review examines current applications, challenges, and future potential of digital twin technology in diabetes prediction and management. PubMed, Scopus, Web of Science, and IEEE Xplore databases were searched for peer reviewed articles (2015-2024) on DT applications in diabetes care, predictive modelling, and therapeutic optimisation. Critical synthesis compared methodological approaches, performance metrics, and implementation challenges. DT demonstrate variable but promising potential through glucose prediction, personalised insulin dosing, dietary optimisation, and complication risk assessment, integrating continuous glucose monitoring, wearable sensors, and machine learning algorithms. Evidence quality varies substantially, with most studies representing proof-of-concept or pilot implementations. Implementation faces data privacy concerns, validation requirements, and integration complexities. Critical gaps exist in long-term effectiveness, algorithmic bias mitigation, and generalisability to underserved populations. DT technology represents an evolving paradigm towards precision diabetes care. However, rigorous clinical validation, addressing equity concerns, and establishing sustainable implementation frameworks remain essential for widespread adoption.