Canadian Researchers Develop AI Curriculum Framework for Family Medicine Education

Bridging the AI gap in medicine: New framework targets family doctor education

A team of Canadian researchers has developed a curriculum framework to help train future family physicians in the use of artificial intelligence (AI), addressing a critical gap in medical training as digital tools become more common in patient care. Published in JMIR Medical Education, the study, "Curriculum Framework for AI Training in Postgraduate Family Medicine Education (AIFM-ed): Mixed Methods Study," introduces the AIFM-ed framework to guide the integration of AI into family medicine training programs. As the health care system evolves, many medical professionals feel unprepared for the growing influence of AI in diagnostics, treatment, and patient management. This newly published framework co-led by Dr. Samira A Rahimi and her trainee Raymond Tolentino offers a structured road map for medical educators to bridge that gap. Developed through a comprehensive review of existing AI education models and in-depth consultations with experts and residents from across Canada, the AIFM-ed framework outlines five core components: need and purpose of the curriculum, learning objectives, curriculum content, organization of curriculum content, and implementation aspects of the curriculum. With every medical school being different, the framework is designed to be flexible, so it can be adapted to each program's specific needs and resources. "AI is rapidly transforming health care, and yet, most medical curricula haven't caught up. AIFM-ed framework provides a critical foundation to ensure that our future physicians are ready for the realities of digitally enabled care," said Dr. Samira A. Rahimi, Canada Research Chair of AI and Advanced Digital Primary Health Care at McGill University and Mila-Quebec AI Institute. Raymond Tolentino, a recent master of science graduate of Dr. Rahimi's lab and co-lead on the study, emphasized how important this work is for preparing future doctors. "We're not just teaching new skills -- we're making sure family doctors feel confident using AI to support better, safer patient care," he said. The next step is for institutions to pilot the framework and assess its impact on postgraduate medical training. By doing so, they can ensure that the next generation of family doctors is not only clinically competent but also technologically fluent. As AI continues to reshape medicine, this framework represents a proactive step toward preparing doctors for the future of digital health.

Bridging the AI Gap in Medicine: New Framework Targets Family Doctor Education | Newswise

Newswise -- (Toronto, April 28, 2025) A team of Canadian researchers has developed a curriculum framework to help train future family physicians in the use of artificial intelligence (AI), addressing a critical gap in medical training as digital tools become more common in patient care. Published in JMIR Medical Education, the study, "Curriculum Framework for AI Training in Postgraduate Family Medicine Education (AIFM-ed): Mixed Methods Study," introduces the AIFM-ed framework to guide the integration of AI into family medicine training programs. As the health care system evolves, many medical professionals feel unprepared for the growing influence of AI in diagnostics, treatment, and patient management. This newly published framework co-led by Dr Samira A Rahimi and her trainee Raymond Tolentino offers a structured road map for medical educators to bridge that gap. Developed through a comprehensive review of existing AI education models and in-depth consultations with experts and residents from across Canada, the AIFM-ed framework outlines five core components: need and purpose of the curriculum, learning objectives, curriculum content, organization of curriculum content, and implementation aspects of the curriculum. With every medical school being different, the framework is designed to be flexible, so it can be adapted to each program's specific needs and resources. "AI is rapidly transforming health care, and yet, most medical curricula haven't caught up. AIFM-ed framework provides a critical foundation to ensure that our future physicians are ready for the realities of digitally enabled care," said Dr Samira A Rahimi, Canada Research Chair of AI and Advanced Digital Primary Health Care at McGill University and Mila-Quebec AI Institute. Raymond Tolentino, a recent master of science graduate of Dr Rahimi's lab and co-lead on the study, emphasized how important this work is for preparing future doctors. "We're not just teaching new skills -- we're making sure family doctors feel confident using AI to support better, safer patient care," he said. The next step is for institutions to pilot the framework and assess its impact on postgraduate medical training. By doing so, they can ensure that the next generation of family doctors is not only clinically competent but also technologically fluent. As AI continues to reshape medicine, this framework represents a proactive step toward preparing doctors for the future of digital health. JMIR Publications is a leading open access publisher of digital health research and a champion of open science. With a focus on author advocacy and research amplification, JMIR Publications partners with researchers to advance their careers and maximize the impact of their work. As a technology organization with publishing at its core, we provide innovative tools and resources that go beyond traditional publishing, supporting researchers at every step of the dissemination process. Our portfolio features a range of peer-reviewed journals, including the renowned Journal of Medical Internet Research. To learn more about JMIR Publications, please visit jmirpublications.com or connect with us via Twitter, LinkedIn, YouTube, Facebook, and Instagram. Head office: 130 Queens Quay East, Unit 1100, Toronto, ON, M5A 0P6 Canada Media contact: [email protected] The content of this communication is licensed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, published by JMIR Publications, is properly cited.