Artificial intelligence for diabetes: Enhancing prevention, diagnosis, and effective management

Abstract

Diabetes, a major cause of premature mortality, affects millions globally, with its prevalence increasing due to lifestyle factors and aging populations. This systematic review explores the role of Artificial Intelligence (AI) in enhancing the prevention, diagnosis, and management of diabetes, highlighting the potential for personalised and proactive healthcare. A structured four-step method was used, including extensive literature searches, specific inclusion and exclusion criteria, data extraction from selected studies focusing on AI's role in diabetes, and thorough analysis to identify specific domains and functions where AI contributes significantly. Through examining 43 experimental studies, AI has been identified as a transformative force across eight key domains in diabetes care: 1) Diabetes Management and Treatment, 2) Diagnostic and Imaging Technologies, 3) Health Monitoring Systems, 4) Developing Predictive Models, 5) Public Health Interventions, 6) Lifestyle and Dietary Management, 7) Enhancing Clinical Decision-Making, and 8) Patient Engagement and Self-Management. Each domain showcases AI's potential to revolutionise care, from personalising treatment plans and improving diagnostic accuracy to enhancing patient engagement and predictive healthcare. AI's integration into diabetes care offers personalised, efficient, and proactive solutions. It enhances care accuracy, empowers patients, and provides better understanding of diabetes management. However, the successful implementation of AI requires continued research, data security, interdisciplinary collaboration, and a focus on patient-centred solutions. Education for healthcare professionals and regulatory frameworks are also crucial to address challenges like algorithmic bias and ethics. AI in diabetes care promises improved health outcomes and quality of life through personalised and proactive healthcare. Future efforts should focus on continued investment, ensuring data security, fostering interdisciplinary collaboration, and prioritising patient-centred solutions. Regular monitoring and evaluation are essential to adjust strategies and understand long-term impacts, ensuring AI's ethical and effective integration into healthcare.

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