Applying Machine Learning to the Health Industry

Abstract

Recent developments in ML and AI have made great leaps in several areas, including the prediction and identification of health emergencies, disease populations, disease states, and immune responses. The use of ML-based techniques is rapidly expanding in healthcare settings, despite ongoing skepticism about their practical use and interpretation of outcomes. Here, with examples, we give a concise introduction to learning algorithms and methodologies based on machine learning, covering topics such as supervised, unsupervised, and reinforcement learning. The second part of the article is devoted to the use of ML in many areas of medicine, such as neuroimaging, genetics, EHRs, and radiology. Additionally, we offer recommendations for future uses of ML and quickly go over the hazards and difficulties of applying it to healthcare, including system privacy and ethical considerations.