Machine Learning (ML) in Medicine: Review, Applications, and Challenges

TLDR

Artificial intelligence and machine learning have rapidly progressed across industries, with ML extracting patterns from raw data to simulate human problem‑solving. The paper aims to provide researchers with a comprehensive understanding of machine learning and its applications in healthcare. The authors propose a taxonomy that classifies healthcare ML schemes by data preprocessing, learning type, evaluation approach, and application domain, and review representative studies. The review helps researchers familiarize with recent ML medical research, recognize challenges and limitations, and identify future directions.

Abstract

Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.