Insights from teaching artificial intelligence to medical students in Canada
Open accessAbstract
Artificial intelligence (AI) in medicine can potentially create workplace efficiencies and aid in clinical decision making. To guide AI applications safely, clinicians need some understanding of AI. Numerous commentaries advocate for AI concepts to be taught 1 , such as interpreting AI models and validation processes 2 . However, few structured programs have been implemented, especially on national scales. Pinto Dos Santos et al 3 . surveyed 263 medical students and 71% agreed they needed AI training. Teaching AI to medical audiences requires nuanced design to balance technical and non-technical concepts for learners who typically have a broad range of prior knowledge. We describe our experiences delivering an AI workshop series to three cohorts of medical students and make recommendations for future AI medical education based on this.
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Few structured programs thoroughly address both the practical and ethical dimensions of AI in medicine.
“Few structured programs thoroughly address both the practical and ethical dimensions of AI in medicine3–10.”
From Developing a Canadian artificial intelligence medical curriculum using a Delphi study (2024) · cites this paper as [9]
The Introduction says few structured AI programs have been implemented, especially at national scale, and that teaching requires balancing technical and non-technical concepts. The Successes section describes practical instruction and discussion of bias, but the paper does not establish that few existing programs thoroughly address both practical and ethical dimensions. (LLM, conf=0.85)
Experimental — accuracy not yet measuredA core set of AI competencies has not been identified, although numerous guides and suggested topics exist.
“Currently, a core set of AI competencies has not been identified, although numerous guides and suggested topics exist9,10.”
From Developing a Canadian artificial intelligence medical curriculum using a Delphi study (2024) · cites this paper as [9]
The Recommendations section calls for identifying core AI competencies, supporting the implication that an agreed core set is lacking. The Introduction says numerous commentaries suggest AI concepts to teach, and the Curriculum lists topics, but the paper does not establish that numerous guides exist. (LLM, conf=0.82)
Experimental — accuracy not yet measuredHu et al. implemented an AI training curriculum for Canadian medical undergraduates focusing on workshops and project feedback, aligning with our inclusion of elements like applying AI models to clinical decision-making (A2) and developing strategies to mitigate biases (E8).
“Hu et al. implemented an AI training curriculum for Canadian medical undergraduates focusing on workshops and project feedback, aligning with our inclusion of elements like applying AI models to clinical decision-making (A2) and developing strategies to mitigate biases (E8)9.”
From Developing a Canadian artificial intelligence medical curriculum using a Delphi study (2024) · cites this paper as [9]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredFinancial barriers, resistance of educators to change curricula, lack of staff with teaching experience in AI, and limited digitalization are some of the main barriers for implementing AI into the medical curriculum.
“Despite the support for including AI education, financial barriers, resistance of educators to change curricula, lack of staff with teaching experience in AI, and limited digitalization are some of the main barriers for implementing AI into the medical curriculum [3,10,12,13].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [10]
Several groups have investigated implementing ad-hoc extracurricular programs to improve AI literacy.
“Several groups have investigated implementing ad-hoc extracurricular programs to improve AI literacy, such as the workshops led by Hu et al. and the Artificial Intelligence Curriculum for Residents (AI-RADS) program at Dartmouth [10,11].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [10]
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- Authors
- Hu, R., Fan, K. Y., Pandey, P., Hu, Z., Yau, O., Teng, M., Wang, P., Li, T., Ashraf, M., & Singla, R.
- Affiliations
- University of British Columbia (CA)
- University of Toronto (CA)
- Queen's University (CA)
- Corresponding author
- Hu, Ricky
- ORCID
- Venue
- Communications Medicine
- Published
- 2022-06-03
- Volume
- 2
- Issue
- 1
- Pages
- 63-63
- ISSN
- 2730-664X (ISSN-L)
- PMC
- PMC9166802
- OpenAlex
- W4281606820
- Cited by
- 58
- References
- 14
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 16 · 2023: 19 · 2022: 1
- FWCI
- 3.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Communications Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Funder: University of British Columbia’s Biomedical Imaging and Artificial Intelligence Research Cluster Provided funding for logistic support.
- 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
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence in healthcare, medical education, medical students, clinical decision-making, AI validation, curriculum development
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