The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Why We Needn’t Fear the Machines: Opportunities for Medicine in a Machine Learning World
Abstract
Recently in medicine, the accuracy of machine learning models in predictive tasks has started to meet or exceed that of board-certified specialists. The ability to automate cognitive tasks using software has raised new questions about the future role of human physicians in health care. Emerging technologies can displace people from their jobs, forcing them to learn new skills, so it is clear that this looming challenge needs to be addressed by the medical education system. While current medical education seeks to prepare the next generation of physicians for a rapidly evolving health care landscape to meet the needs of the communities they serve, strategic decisions about disruptive technologies should be informed by a deeper investigation of how machine learning will function in the context of medicine. Understanding the purpose and strengths of machine learning elucidates its implications for the practice of medicine. An economic lens is used to analyze the interaction between physicians and machine learning. According to economic theory, competencies that are complementary to machine prediction will become more valuable in the future, while competencies that are substitutes for machine prediction will become less valuable. Applications of machine learning to highly specific cognitive tasks will increase the performance and value of health professionals, not replace them. To train physicians who are resilient in the face of potential labor market disruptions caused by emerging technologies, medical education must teach and nurture unique human abilities that give physicians a comparative advantage over computers.
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In response, physicians adapted their roles to best match their tools to further advance patient care.
“In response, physicians adapted their roles to best match their tools to further advance patient care. 17”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [17]
In order to be resilient to market disruptions from emerging technologies, physician training programs will require change.
“In order to be resilient to these market disruptions, physician training programs will require change [9].”
From Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum (2022) · cites this paper as [9]
Citations
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Artificial Intelligence in Undergraduate Medical Education: A Scoping Review
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Self-citationArtificial intelligence education programs for health care professionals: Scoping review
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“17 However, with the rapid advancement of AI, physicians face an unprecedented stress to learn entire new skill sets and to manage an exponential growth of medical knowledge.”
BackgroundNeeds, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum
Grunhut, J., Marques, O., & Wyatt, A. T. M. (2022) · JMIR Medical Education · doi:10.2196/35587 · Cited by 166
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Machine learning to extract communication and history‐taking skills in OSCE transcripts
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Artificial scholarship: LLMs in health professions education research
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Metadata
- Authors
- Li, D., Kulasegaram, K., & Hodges, B. D.
- Affiliations
- University of Toronto (CA)
- University Health Network (CA)
- Corresponding author
- Li, David
- Venue
- Academic Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-05
- Volume
- 94
- Issue
- 5
- Pages
- 623-625
- ISSN
- 1040-2446 (ISSN-L)
- OpenAlex
- W2920604814
- Cited by
- 50
- References
- 8
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 9 · 2023: 5 · 2022: 7 · 2021: 11 · 2020: 5 · 2019: 2
- FWCI
- 1.98
- 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
- Social Sciences › Education › Innovations in Medical Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- machine learning in medicine, medical education, physician role, disruptive technologies, labor market disruption, future of health care, physician resilience
- MeSH terms
- Adult, Attitude to Computers, Computer-Assisted Instruction, Education, Medical, Fear, Female, Humans, Male, Middle Aged, Physicians, Students, Medical, Young Adult
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