Applications of Machine Learning in Healthcare: An Overview

Abstract

Traditional research techniques do not work well in the dynamic environment of big data in the healthcare industry, which is characterized by enormous numbers, complexity, and speeds. Methods that can effectively estimate models across massive datasets of medical usage data, health findings, private computer data, and records from many other sources are critically needed. The goal of machine learning (ML), a branch of artificial intelligence (AI), is to increase the efficiency and precision of healthcare. Applications of machine learning (ML) are significantly changing the healthcare industry. Big data and improved computing power present an opportunity to apply ML algorithms to benefit healthcare. The healthcare curriculum has to incorporate a variety of ML-related topics so that medical personnel may successfully direct and analyze research in this field. Machine learning will revolutionize nearly every aspect of the healthcare profession as it stands today. To understand the various healthcare applications of this technology, it is necessary to examine the research that has been done in this area. This enumerative article provides a thorough review of machine learning in healthcare, covering its terminology, basic concepts, and present and potential uses.

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