Introduction: Diabetes is a disease that troubles almost six hundred million individuals in the world and causes a significant level of morbidity, mortality, and increased healthcare expenditures. Delayed interventions, low adherence, and poor resources (especially in low and middle-income countries) restrict traditional prevention and management strategies. Methods: The review is a synthesis of the recent literature on how artificial intelligence (AI), machine learning (ML), and digital health technologies can change the way diabetes is managed. Peer-reviewed databases were used to identify sources with a focus on the use of the applications in the early diagnosis, risk prediction, self-management, and clinical decision support. Results: Such AI-based technologies as predictive analytics, clinical decision support systems, and mobile health can improve monitoring, glycemic control, and prevention of complications. Self-management has been enhanced by employing smart technologies, like continuous glucose monitors, insulin pumps, bolus calculators, and smartphone platforms, and telemedicine enhances access to care and coordination. Discussion: In spite of the positive results, there are still problems, such as the quality of data, involvement of patients, integration of clinical activities, and ethical aspects, such as bias, privacy, and accountability. Multidisciplinary employment, ease of use, and comparable datasets would triumph over these issues. Conclusion: Patient-centered, convenient, and proactive interventions are provided through AI and intelligent health technologies to control diabetes. They can make a significant contribution to the outcome improvement and minimization of the global burden of diabetes with the help of proper integration.
Maaz et al. (Wed,) studied this question.