Differences in Expert Perspectives on AI Training in Medical Education: Secondary Analysis of a Multinational Delphi Study
In this secondary analysis of a multinational Delphi study, experts from low- and middle-income countries were less likely than those from high-income countries to consider artificial intelligence (AI) learning outcomes mandatory in preregistration medical education, potentially reflecting underlying global inequalities in medical AI education and highlighting the need for adaptable AI competency frameworks.
Authors
- Qi Chwen Ong (ORCID: https://orcid.org/0009-0009-5621-5071)
- Josip Car (ORCID: https://orcid.org/0000-0001-8969-371X)
- Chin‐Siang Ang (ORCID: https://orcid.org/0000-0003-1868-7827)
- Nai Ming Lai (ORCID: https://orcid.org/0000-0002-4204-3098)
- Rifat Atun (ORCID: https://orcid.org/0000-0002-1531-5983)
Institutions
- Harvard University (US)
- Nanyang Technological University (SG)
- King's College London (GB)
- Singapore Centre for Environmental Life Sciences Engineering (SG)
- Taylor's University (MY)
- Harvard Global Health Institute (US)
- Imperial College London (GB)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-05-09
- DOI
- https://doi.org/10.2196/72186
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65