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. Based on 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.
D. Li is research assistant, University of Toronto, Toronto, Ontario, Canada.
K. Kulasegaram is scientist and assistant professor, Department of Family and Community Medicine, Wilson Centre, University Health Network and Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
B.D. Hodges is executive vice president and chief medical officer, University Health Network, and professor, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
Funding/Support: None reported.
Other disclosures: None reported.
Ethical approval: Reported as not applicable.
Correspondence should be addressed to David Li, University of Toronto, 27 King’s College Circle, Toronto, Ontario M5S 1A1 Canada; email: firstname.lastname@example.org.