Data Science as a Core Competency in Undergraduate Medical Education in the Age of Artificial Intelligence in Health Care
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Abstract
The increasingly sophisticated and rapidly evolving application of artificial intelligence in medicine is transforming how health care is delivered, highlighting a need for current and future physicians to develop basic competency in the data science that underlies this topic. Medical educators must consider how to incorporate central concepts in data science into their core curricula to train physicians of the future. Similar to how the advent of diagnostic imaging required the physician to understand, interpret, and explain the relevant results to patients, physicians of the future should be able to explain to patients the benefits and limitations of management plans guided by artificial intelligence. We outline major content domains and associated learning outcomes in data science applicable to medical student curricula, suggest ways to incorporate these themes into existing curricula, and note potential implementation barriers and solutions to optimize the integration of this content.
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AI-related skills such as computational thinking, data ethics, and interdisciplinary collaboration cannot be taught as optional extras and must be integrated into the fundamental structure of medical training.
“There is an increasing recognition that AI-related skills—such as computational thinking, data ethics, and interdisciplinary collaboration—cannot be taught as optional extras. They must be integrated into the fundamental structure of medical training [8–10].”
From Institutionalizing convergence education for medical artificial intelligence (2025) · cites this paper as [8]
The Conclusions (page not provided) call data science and AI a core competency and urge their integration into undergraduate medical curricula. The Ethics and Cybersecurity section also places health-data ethics in required training. The paper discusses collaboration with data and IT professionals to deliver teaching, but the retrieved passages do not establish interdisciplinary collaboration or computational thinking as required student skills. (LLM, conf=0.84)
Experimental — accuracy not yet measuredHealth care is increasingly data driven, with vast amounts of health data generated for various purposes, from clinical decision-making to quality improvement and research.
“Health care is increasingly data driven, with vast amounts of health data generated for various purposes, from clinical decision-making to quality improvement and research [44].”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [44]
Explaining technology is not new; it is a challenge encountered whenever complex technology integrates into medical care.
“Explaining technology is not new; it is a challenge encountered whenever complex technology integrates into medical care [44].”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [44]
There is increasing agreement within the literature on the need for health informatics skills, as shown in multiple recently published papers suggesting various educational methods and frameworks.
“There is increasing agreement within the literature on this need as shown in multiple recently published papers suggesting various educational methods and frameworks to accomplish this [39,44].”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [44]
A basic understanding in data science seems necessary alongside traditional medical expertise to navigate changing informational landscapes.
“To navigate these changing informational landscapes, a basic understanding in data science seems necessary alongside traditional medical expertise [56].”
From Comparison of the Performance of GPT-3.5 and GPT-4 With That of Medical Students on the Written German Medical Licensing Examination: Observational Study (2024) · cites this paper as [56]
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References 33 · Cited by 52
Research works printed but not linked: 1
- [25]HIPAA Training Requirements The HIPAA Journal 2023-05-05 https://www.hipaajournal.com/hipaa-training-requirements/
- Authors
- Seth, P., Hueppchen, N., Miller, S. D., Rudzicz, F., Ding, J., Parakh, K., & Record, J. D.
- Affiliations
- McMaster University (CA)
- Johns Hopkins University (US)
- Dalhousie University (CA)
- Western University (CA)
- Georgetown University (US)
- Corresponding author
- Seth, Puneet
- ORCID
- Venue
- JMIR Medical Education
- Publisher
- JMIR Publications Inc.
- Published
- 2023-07-11
- Volume
- 9
- Pages
- e46344
- ISSN
- 2369-3762 (ISSN-L)
- PMC
- PMC10369309
- OpenAlex
- W4382046279
- Cited by
- 52
- References
- 33
- Citations by year
- 2026: 16 · 2025: 20 · 2024: 11 · 2023: 5
- FWCI
- 2.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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
- data science education, artificial intelligence in healthcare, undergraduate medical education, medical curriculum, physician competence, learning outcomes, curriculum integration, implementation barriers, medical students
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