Exercising the application of Artificial intelligence for better management of Diabetes mellitus: A Review

Abstract

Background: Diabetes Mellitus, also referred to as diabetes, represents a chronic condition hallmarked by elevated blood sugar levels. In the realm of AI, understanding and managing this ailment involves comprehending the intricate interplay of factors contributing to its onset and progression. This encompasses the body's inability to produce sufficient insulin or utilize it effectively. Insulin, a critical substance emanating from the pancreas, governs glucose absorption into cells and its subsequent conversion into vital energy.

Objectives: The AI perspective involves leveraging advanced algorithms and data analytics to dissect the multifaceted aspects of diabetes, categorizing it into various forms like "type 1 DM," "type 2 DM," and "gestational DM."

Effective blood sugar management, a core focus in diabetes care, entails maintaining blood sugar within optimal levels through a balanced diet, regular physical activity, appropriate medications if prescribed, and vigilant blood sugar monitoring.

Method: From an AI lens, predictive models and machine learning algorithms aid in anticipating blood sugar trends, allowing for timely adjustments in treatment plans. Routine comprehensive health evaluations, encompassing specialized eye and foot examinations, are pivotal in AI-driven diabetes management, aiming to identify and proactively address potential complications.

Result: This article provides a better understanding of the link between, Information, advancement of knowledge, and the importance of bibliometric analysis in various research fields. Future research in the field of AI applications for controlling diabetes mellitus holds great promise for advancing disease management and enhancing patient outcomes

Discussion: Real-time decision support systems powered by AI can provide immediate guidance to both healthcare providers and patients, leveraging continuous data from wearable devices, glucose monitors, electronic health records, and other sources.