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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer – Medknow Publications
2023-02-01
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| Series: | Singapore Medical Journal |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.11622/smedj.2021054 |
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| _version_ | 1823861457203232768 |
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| author | Jordan Zheng Ting Sim Qi Wei Fong Weimin Huang Cher Heng Tan |
| author_facet | Jordan Zheng Ting Sim Qi Wei Fong Weimin Huang Cher Heng Tan |
| author_sort | Jordan Zheng Ting Sim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-35a23f0dd7fb48c19797b0c3c685fcfe |
| institution | Kabale University |
| issn | 0037-5675 2737-5935 |
| language | English |
| publishDate | 2023-02-01 |
| publisher | Wolters Kluwer – Medknow Publications |
| record_format | Article |
| series | Singapore Medical Journal |
| spelling | doaj-art-35a23f0dd7fb48c19797b0c3c685fcfe2025-02-09T13:42:57ZengWolters Kluwer – Medknow PublicationsSingapore Medical Journal0037-56752737-59352023-02-01642919710.11622/smedj.2021054Machine learning in medicine: what clinicians should knowJordan Zheng Ting SimQi Wei FongWeimin HuangCher Heng TanWith 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.https://journals.lww.com/10.11622/smedj.2021054algorithmsartificial intelligencedeep learningmachine learningneural networks |
| spellingShingle | Jordan Zheng Ting Sim Qi Wei Fong Weimin Huang Cher Heng Tan Machine learning in medicine: what clinicians should know Singapore Medical Journal algorithms artificial intelligence deep learning machine learning neural networks |
| title | Machine learning in medicine: what clinicians should know |
| title_full | Machine learning in medicine: what clinicians should know |
| title_fullStr | Machine learning in medicine: what clinicians should know |
| title_full_unstemmed | Machine learning in medicine: what clinicians should know |
| title_short | Machine learning in medicine: what clinicians should know |
| title_sort | machine learning in medicine what clinicians should know |
| topic | algorithms artificial intelligence deep learning machine learning neural networks |
| url | https://journals.lww.com/10.11622/smedj.2021054 |
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