Medical Education Must Move From the Information Age to the Age of Artificial Intelligence
Abstract
Noteworthy changes coming to the practice of medicine require significant medical education reforms. While proposals for such reforms abound, they are insufficient because they do not adequately address the most fundamental change-the practice of medicine is rapidly transitioning from the information age to the age of artificial intelligence. Increasingly, future medical practice will be characterized by: the delivery of care wherever the patient happens to be; the provision of care by newly constituted health care teams; the use of a growing array of data from multiple sources and artificial intelligence applications; and the skillful management of the interface between medicine and machines. To be effective in this environment, physicians must work at the top of their license, have knowledge spanning the health professions and care continuum, effectively leverage data platforms, focus on analyzing outcomes and improving performance, and communicate the meaning of the probabilities generated by massive amounts of data to patients, given their unique human complexities. The authors believe that a "reboot" of medical education is required that makes better use of the findings of cognitive psychology and pays more attention to the alignment of humans and machines in education and practice. Medical education needs to move beyond the foundational biomedical and clinical sciences. Systematic curricular attention must focus on the organization of professional effort among health professionals, the use of intelligence tools involving large data sets, and machine learning and robots, all the while assuring the mastery of compassionate care.
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To date, there has been no published framework to guide teaching, learning, and assessing health care students and practitioners in the use of AI in health care.
“While numerous authors have called for structured and intentional learning programs, to date, there has been no published framework to guide teaching, learning, and assessing health care students and practitioners in this emerging and transformative domain [7,9-12].”
From Considering Clinician Competencies for the Implementation of Artificial Intelligence–Based Tools in Health Care: Findings From a Scoping Review (2022) · cites this paper as [12]
Concerns about algorithmic opacity, reproducibility, and the "black-box" nature of clinical decision-support systems had already been accumulating in the medical AI literature throughout the late 2010s.
“Concerns about algorithmic opacity, reproducibility, and the “black-box” nature of clinical decision-support systems had already been accumulating in the medical AI literature throughout the late 2010s [7, 11, 12]”
From Ethical and responsible use of artificial intelligence in medical education (2026) · cites this paper as [11]
The risk is that a field can appear, by sheer publication volume, to be addressing responsible AI use while its actual citation-weighted center of gravity continues to reward technical demonstration over governance scholarship.
“The risk is that a field can appear, by sheer publication volume, to be addressing responsible AI use while its actual citation-weighted center of gravity continues to reward technical demonstration over governance scholarship [11].”
From Ethical and responsible use of artificial intelligence in medical education (2026) · cites this paper as [11]
AI in medical education raises concerns about reproducibility, transparency, necessity for protective oversight, and accuracy of produced content.
“With the prominent reliance on secondary datasets that may contain systemic biases, concerns have been raised regarding reproducibility, transparency of generated content, necessity for protective oversight, and accuracy of produced content [7, 11].”
From Ethical and responsible use of artificial intelligence in medical education (2026) · cites this paper as [11]
A limited number of articles have discussed or reviewed the current use of AI in medical education.
“To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education.”
From Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review (2019) · cites this paper as [10]
The practice of medicine is transitioning from the Age of Information to the Age of AI.
“Wartman et al. noted that the practice of medicine is transitioning from the Age of Information to the Age of AI (55).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [55]
Understanding the need for social skills will help identify biases and heuristics that impact decision-making as well as help physicians frame choices, understand context, and have neutral but meaningful conversations with patients.
“Understanding this need for social skills will help identify biases and heuristics that impact decision-making as well as help physicians frame choices, understand context, and have neutral but meaningful conversations with patients (55).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [55]
Clinical practice is changing from the information age to the AI age.
“Paranjape et al. and Wartman and Combs believe that clinical practice is changing from the information age to the AI age [3,4].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [4]
Numerous experts have advocated for the incorporation of AI training and literacy into medical curricula.
“Numerous experts have advocated for the incorporation of AI training and literacy into medical curricula [3–6].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [4]
Among the 6 articles mentioning plans for curricular change, there is no indication of published articles following up on those plans.
“Of the 6 articles mentioning plans for curricular change, there is no indication of published articles following up on these plans.38,44,50,51”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [51]
AI could make healthcare providers complacent and less likely to check results and challenge errors.
“A related concern is that AI could make healthcare providers complacent and less likely to check results and challenge errors.34”
From Artificial intelligence: opportunities and implications for the health workforce (2020) · cites this paper as [34]
Concerns have been raised that datasets used to train AI systems have inherent biases and are often not representative of the wider population.
“Concerns have been raised that datasets used to train AI systems have inherent biases and are often not representative of the wider population.34”
From Artificial intelligence: opportunities and implications for the health workforce (2020) · cites this paper as [34]
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- Authors
- Wartman, S. A., & Combs, C. D.
- Affiliations
- Eastern Virginia Medical School (US)
- Corresponding author
- Wartman, Steven A.
- Venue
- Academic Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-08
- Volume
- 93
- Issue
- 8
- Pages
- 1107-1109
- ISSN
- 1040-2446 (ISSN-L)
- OpenAlex
- W2766894906
- Cited by
- 585
- References
- 1
- Citations by year
- 2026: 106 · 2025: 114 · 2024: 118 · 2023: 94 · 2022: 54 · 2021: 34 · 2020: 38 · 2019: 18 · 2018: 7 · 2013: 1
- FWCI
- 16.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academic Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Health and Medical Education
- Keywords
- artificial intelligence in healthcare, medical education reform, human-machine interaction, cognitive psychology, healthcare teams, data-driven medicine, machine learning, health professions education, compassionate care, physician role
- MeSH terms
- Artificial Intelligence, Clinical Competence, Cooperative Behavior, Education, Medical, Humans
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