The Intersection of AI and IoT for Clinical Digitalization in Healthcare Domain

People in modern world suffer from a multitude of diseases, the most common of which is diabetes, which affects most senior citizens. Diabetes is associated with kidney problems, cancer, and heart disease. Combining Internet of Things (IoT) with Artificial intelligence (AI) to improve the functionality of remote systems for monitoring health for doctors, patients and patient attendants is one of the most significant technological advancements to date. The integration of AI and IoT also improves clinical workflow efficiency. Intelligent hospital management systems use AI-powered IoT networks to increase resource allocation, shorten wait times, and improve outcomes for patients. Predictive analytics helps to estimate the number of patients admitted and manage hospital inventories effectively. This paper explores the development of an innovative patient monitoring system that focuses on analyzing patient diabetes to facilitate timely medical interventions. In this research work applied three types of Learning methods and subsequently some main parameters were utilized to assess the efficiency of methods. The different machine learning and Deep learning algorithms are compared with one another and created new model with IoT advantages.