Artificial intelligence and digital twins: revolutionizing diabetes care for tomorrow

Abstract

Artificial intelligence (AI) and digital twin technologies exhibit significant potential in analyzing and integrating multidimensional datasets and offer novel perspectives for the management of chronic diseases including diabetes. These technologies offer opportunities for personalizing treatment and potentially reversing the conditions. This review systematically evaluated the advantages and limitations of AI applications, potential for predictive analytics in formulating personalized management strategies, and practical roles of AI and digital twin technologies in diabetes diagnosis and treatment. Special attention was given to their strengths and weaknesses in disease prediction, early detection, and development of individualized management strategies.

AI algorithms have demonstrated great efficiency in analyzing large datasets, aiding in the early identification and intervention of prediabetes. Machine learning algorithms, including deep learning neural networks, integrate lifestyle, genetic, and other influencing factors to accurately predict the progression of prediabetes to diabetes. Moreover, AI-driven wearable devices and mobile applications provide real-time monitoring and personalized guidance, thereby effectively mitigating diabetes. This study also explored the challenges of integrating AI and digital twin technologies into clinical practice for diabetes management and broader healthcare domains, focusing on data privacy, need for diverse and comprehensive datasets, and the importance of integrating AI tools into clinical workflows.