AIFM-ed Curriculum Framework for Postgraduate Family Medicine Education on Artificial Intelligence: Mixed Methods Study
Using the results of this study, we developed the AIFM-ed curriculum framework for AI training in postgraduate family medicine education. This framework serves as a structured guide for integrating AI competencies into medical education, ensuring that future family physicians are equipped with the necessary skills to use AI effectively in their clinical practice. Future research should focus on the validation and implementation of the AIFM-ed framework within family medicine education. Institutions also are encouraged to consider adapting the AIFM-ed framework within their own programs, tailoring it to meet the specific needs of their trainees and health care environments.
Authors
- Samira Abbasgholizadeh Rahimi (ORCID: https://orcid.org/0000-0003-3781-1360)
- Fanny Hersson-Edery (ORCID: https://orcid.org/0000-0002-3725-080X)
- Raymond Tolentino (ORCID: https://orcid.org/0000-0002-7758-314X)
- Mark J. Yaffe⃰ (ORCID: https://orcid.org/0000-0001-5488-1972)
Institutions
- McGill University Health Centre (CA)
- Jewish General Hospital (CA)
- St Mary's Hospital Centre (CA)
- Mila - Quebec Artificial Intelligence Institute (CA)
- McGill University (CA)
Publication Details
- Journal
- JMIR Medical Education
- Published
- 2025-04-25
- DOI
- https://doi.org/10.2196/66828
- Citations
- 7
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 4.58