Accepted for/Published in: JMIR Medical Education
Date Submitted: Sep 24, 2024
Date Accepted: Feb 25, 2025
AIFM-ed Curriculum Framework for Family Medicine Postgraduate Education on Artificial Intelligence: a mixed methods study
ABSTRACT
Background:
As healthcare moves to a more digital environment, there is a growing need to train future family doctors on the clinical uses of Artificial Intelligence (AI). However, family medicine training in AI has often been inconsistent or lacking.
Objective:
To develop curriculum framework for family medicine postgraduate education on AI called “Artificial Intelligence for Family Medicine” (AIFM-ed).
Methods:
First, we conducted a comprehensive scoping review on existing AI education frameworks guided by methodological framework developed by Arksey and O’Malley, and Joanna Briggs Institute methodological framework for scoping reviews. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. Next, two national expert panels were conducted. Panelists included family medicine educators and residents knowledgeable in AI from family medicine residency programs across Canada. Participants were purposively sampled, and panels were held via Zoom, recorded, and transcribed. Data was analyzed using content analysis. We followed the Standards for Reporting Qualitative Research for panels.
Results:
An integration of the scoping review results and two panel discussions of 14 participants led to the development of the AIFM-ed curriculum framework for AI training in postgraduate family medicine education with five key elements: 1) need and purpose of the curriculum, 2) learning objectives, 3) curriculum content, 4) organization of curriculum content and 5) implementation aspects of the curriculum.
Conclusions:
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 healthcare environments.
Citation
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