Machine Learning: The Next Paradigm Shift in Medical Education
Abstract
Machine learning (ML) algorithms are powerful prediction tools with immense potential in the clinical setting. There are a number of existing clinical tools that use ML, and many more are in development. Physicians are important stakeholders in the health care system, but most are not equipped to make informed decisions regarding deployment and application of ML technologies in patient care. It is of paramount importance that ML concepts are integrated into medical curricula to position physicians to become informed consumers of the emerging tools employing ML. This paradigm shift is similar to the evidence-based medicine (EBM) movement of the 1990s. At that time, EBM was a novel concept; now, EBM is considered an essential component of medical curricula and critical to the provision of high-quality patient care. ML has the potential to have a similar, if not greater, impact on the practice of medicine. As this technology continues its inexorable march forward, educators must continue to evaluate medical curricula to ensure that physicians are trained to be informed stakeholders in the health care of tomorrow.
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The reviewed literature suggests a consensus on the importance of AI education in the medical curriculum but a void in actual planning and implementation of such education.
“Our results show that the literature suggests a consensus of opinions regarding the importance of AI education in medical curriculum but a void in actual planning and implementation of this.43,46,48”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [48]
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“43,46,48 Of the 6 articles mentioning plans for curricular change, there is no indication of published articles following up on these plans.”
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- Authors
- James, C. A., Wheelock, K. M., & Woolliscroft, J. O.
- Affiliations
- University of Michigan (US)
- Yale University (US)
- Corresponding author
- James, Cornelius A.
- Venue
- Academic Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-01-25
- Volume
- 96
- Issue
- 7
- Pages
- 954-957
- ISSN
- 1040-2446 (ISSN-L)
- OpenAlex
- W3125170355
- Cited by
- 58
- References
- 11
- Citations by year
- 2025: 14 · 2024: 15 · 2023: 14 · 2022: 12 · 2021: 3
- FWCI
- 3.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academic Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Health Sciences › Health Professions › General Health Professions › Health and Medical Education
- Keywords
- machine learning, medical education, medical curriculum, clinical decision support, physician training, evidence-based medicine, clinical implementation of AI
- MeSH terms
- Clinical Decision-Making, Machine Learning, Stakeholder Participation, COVID-19 Testing, Aged, Algorithms, Clinical Trials as Topic, Curriculum, Delivery of Health Care, Diabetic Retinopathy, Diagnostic Imaging, Education, Medical, Female, Humans, Male, Physician-Patient Relations, Physicians, United States, United States Food and Drug Administration, Liability, Legal, Evidence-Based Medicine, History, 20th Century
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