Hyperglycaemia, a sign of diabetes mellitus (DM), is a major public health issue that is spreading quickly around the world. It leads to a lot of illness, death, and economic issues. Type 1 diabetes (T1DM), type 2 diabetes (T2DM), and diabetes during pregnancy are the three main forms of diabetes mellitus. Type 2 diabetes is the most common of these. even with improvements in pharmaceutical treatments, management is still difficult and calls for ongoing observation, lifestyle changes, and preventative measures. Recent developments in technology demonstrate how machine learning (ML) and artificial intelligence (AI) are transforming the treatment of diabetes. Automated retinal screening for diabetic retinopathy, clinical decision support systems, telehealth, mobile health apps, and predictive population risk stratification are a few examples of AI uses. Furthermore, glycaemic control and patient quality of life have improved when AI is combined with controlled insulin delivery systems, pumps for insulin, and continuous blood glucose monitoring (CGM). these advances are demonstrated by devices as Glucommander, Dexcom G7, Diab loop, and Medtronic Mini Med. Mobile apps with AI capabilities, such as Glooko, mySugr, and BlueStar, improve patient adherence and engagement even more. there are still difficulties, though, such as poor data quality, little potential validation, computational limitations, and human variables. prospects for the future focus on customized care based on biomarkers, lifestyle, and genetics, deep learning models for medical imaging, and predictive analytics for problems. In general, artificial intelligence is a game-changing technology for managing diabetes, with the promise to improve patient-centred outcomes, diagnosis, treatment, and prevention.
Navneet Pratap Singh Km Monee* (Fri,) studied this question.