Introductory machine learning for medical students: A pilot
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Lack of knowledge in AI among medical school faculty is the single most important hindrance to incorporating AI into the curriculum.
“Based on our results of the previous literature, we found that the lack of knowledge in AI among medical school faculty is the single most important hindrance in incorporating it among the curriculum.34,47,49,55”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [47]
Among the 2 reports of implemented changes, there were no previous reported studies researching what students hope to gain from such changes or what methods would be most effective.
“Among the 2 reports of implemented changes, there were no previous reported studies researching what students hope to gain from such changes or what methods would be most effective.36,47”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [47]
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A scoping review of artificial intelligence in medical education: BEME Guide No. 84
Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R., Hanson, J., Haas, M., Spadafore, M., Grafton-Clarke, C., et al. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2314198 · Cited by 543 · see citing works
Grunhut, J., Wyatt, A. T., & Marques, O. (2021) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205211036836 · Cited by 161 · see citing works
“34,47,49,55 Medical school faculty simply have no understanding of how to implement this change.”
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 · see citing works
Insights from teaching artificial intelligence to medical students in Canada
Hu, R., Fan, K. Y., Pandey, P., Hu, Z., Yau, O., Teng, M., Wang, P., Li, T., Ashraf, M., & Singla, R. (2022) · Communications Medicine · doi:10.1038/s43856-022-00125-4 · Cited by 58 · see citing works
Self-citation
- Authors
- Fan, K. Y., Hu, R., & Singla, R.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Fan, Kevin Y.
- Venue
- Medical Education
- Publisher
- Wiley
- Published
- 2020-08-29
- Volume
- 54
- Issue
- 11
- Pages
- 1042-1043
- ISSN
- 0308-0110 (ISSN-L)
- 1365-2923
- OpenAlex
- W3081852946
- Cited by
- 19
- References
- 0
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 1 · 2022: 4 · 2021: 2
- FWCI
- 0.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Education (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of British Columbia
- 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
- Life Sciences › Neuroscience › Cognitive Neuroscience › Undergraduate Neuroscience Education and Research
- Keywords
- machine learning, medical students, pilot study, artificial intelligence in healthcare
- MeSH terms
- Machine Learning, Educational Measurement, Humans, Students, Medical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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