Machine learning in medicine: what clinicians should know

• Published in: Singapore medical journal Vol. 64; no. 2; pp. 91 - 97
• Main Authors: Ting Sim, Jordan, Fong, Qi, Huang, Weimin, Tan, Cher
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer India Pvt. Ltd 01.02.2023; Medknow Publications and Media Pvt. Ltd; Wolters Kluwer - Medknow
• Subjects: Algorithms; Artificial Intelligence; Computational linguistics; Humans; Language processing; Machine Learning; Medicine; Natural language interfaces; Neural Networks, Computer; Practice; Review; Singapore; deep learning; Algorithms; machine learning; artificial intelligence; neural networks
• ISSN: 0037-5675; 2737-5935
• DOI: 10.11622/smedj.2021054

With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.