Methods for the Recognition of Multisource Data in Intelligent Medicine: A Review and Next-Generation Trends

Abstract

The use of technological innovations in medicine has led to an increase in efficiency in disease recognition and to guide human life by facilitating. Intelligent medical systems can easily diagnose the disease predictions that real physicians may overlook by establishing a connection between the disease and the symptoms. These developments have created the need for new hardware and software technologies to process of big data in medical science. Today, technological support is necessary, especially for patients with neurological disorders such as epilepsy and Alzheimer's, requiring constant attention, care, and treatment. New technological innovations are needed to develop new artificial intelligence modules to process and define the big data from different data sources in digital medical applications. In the applications, innovative technological developments are needed by using artificial intelligence methods to process and define big data from different data sources. This study is about the systematic literature review that uses new generation techniques for a personal, reliable, and sensitive healthcare system that can determine the course of the disease by processing data obtained from different digital sources by the artificial intelligence methods.