AI-Based Approaches for Early Detection and Prevention of Diabetes
Neeta Rai, Poonam R Inamdar, Jisha joseph, Omkar Kokate, Ajinkya Mote, Shivanjali Tagade, Narendra Mukesh Vishwakarma, Aryan Mangesh Vishwakarma, Pooja Malviya, Anjita Singh Thakur, Viren Mandlecha, Piyush Jangid
Diabetes mellitus is a chronic and multifaceted disorder representing a major global health concern. The two primary forms, Type 1 and Type 2 diabetes, differ in origin and progression yet share serious complications, including cardiovascular disease, kidney impairment, and nerve damage. In recent years, Artificial Intelligence (AI) has emerged as a transformative tool for early detection, treatment optimization, and prevention of diabetes-related risks. This review examines the role of AI in diabetes management, focusing on its integration into early diagnostic systems, personalized treatment planning, and risk prediction based on diverse and large datasets. These data sources include medical history, lifestyle patterns, and genetic profiles. AI-based models, particularly machine learning algorithms and neural networks, demonstrate improved accuracy in identifying diabetes and enabling timely intervention. AI-powered wearables and mobile applications support continuous self-monitoring and provide real-time recommendations for glycemic control. Moreover, the application of AI within traditional medical systems such as Ayurveda and Unani shows potential for enhancing diagnostic precision and individualized care. However, challenges persist, including inconsistent data quality, algorithmic limitations, and concerns regarding patient data privacy. Advancements in big data analytics and IoT technologies continue to expand the scope of AI in diabetes care. AI is reshaping diabetes management by improving clinical outcomes, reducing healthcare costs, and contributing to global efforts to mitigate the burden of the disease.