Machine learning in medicine: what clinicians should know

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, physicia...

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Veröffentlicht in:Singapore medical journal Jg. 64; H. 2; S. 91 - 97
Hauptverfasser: Sim, Jordan Zheng Ting, Fong, Qi Wei, Huang, Weimin, Tan, Cher Heng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: India Medknow Publications & Media Pvt Ltd 01.02.2023
Medknow Publications and Media Pvt. Ltd
Wolters Kluwer - Medknow
Wolters Kluwer – Medknow Publications
Ausgabe:2
Schlagworte:
Algorithms
Artificial Intelligence
Computational linguistics
deep learning
Humans
Language processing
Machine Learning
Medicine
Natural language interfaces
neural networks
Neural Networks, Computer
Practice
Review
Review Article
Singapore
deep learning
Algorithms
machine learning
artificial intelligence
neural networks
ISSN:0037-5675, 2737-5935, 2737-5935
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
content type line 23
ISSN:0037-5675
2737-5935
2737-5935
DOI:10.11622/smedj.2021054