Artificial Intelligence for Predicting Diabetes Complications and Management

Abstract

The artificial intelligence (AI) application has now become a drastic way of managing diabetes and has provided unprecedented predictive, diagnostic, and preventive capabilities. The current literature shows how machine learning and computational intelligence methods can be applied to big clinical, lab and lifestyle data in order to discover the individuals who may be under risk of developing complications like diabetic retinopathy, nephropathy and cardiovascular conditions. Artificial intelligence-based systems, risk engines, and ensemble learning algorithms can be used to build individual management strategies and predict the development of the disease and the most efficient treatment regimen. The AI/continuous glucose monitor interface has a connection with real-time data, which enhances the glycemic control and completion of patients. Notwithstanding the dramatic improvements, the data, model interpretation, and relationship to the clinical processes issue remain. There is a chronic examination of recent studies where it is demonstrated that multimodal data, including electronic health records, wearable, and imaging, have the potential to emerge as a better predictive and decision support source of information. Besides, according to AI applications, it is easy to identify the disease early, rank the risks and intervene at a young age, and eventually decrease the disease complication burden. The current AI development in the field of diabetes management suggests the potential possibility of the technology to provide precision medicine and better patient outcomes, and implies that on the contrary, ethical, safe, and clinical valid use is required.

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