A global framework for artificial intelligence education in medicine: international working group recommendations

AI education in medicine remains fragmented, narrowly technical, and disconnected from healthcare realities. Through an international collaboration, we developed a framework spanning seven domains and 24 learning objectives. We reflect on what this process revealed: effective AI education must be clinically grounded, ethically integrated, and implementation-aware across the training continuum. We share key insights, tensions encountered, and lessons for educators in translating this framework into practice.

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Publication Details

Journal
npj Digital Medicine
Published
2026-09-09
DOI
https://doi.org/10.1038/s41746-026-03197-x
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

A global framework for artificial intelligence education in medicine: international working group recommendations

Oded Nov, Bradley J. Erickson, Abhishek Moturu, Arwa Nada et al.
npj Digital Medicine
Artificial Intelligence in Healthcare and Education
article

A global framework for artificial intelligence education in medicine: international working group recommendations

Oded Nov, Bradley J. Erickson, Abhishek Moturu, Arwa Nada, Jamie Fairclough, Piyush Mathur, Laura C. Rosella, Gemma Postill, Píetro Lió, Diana Ferro, Alfonso Limon, Nathan Yung, Yindalon Aphinyanaphongs, Zoryana Salo, Marianne So, Orest Boyko, Hari Mudipalli, Nihal Haque, Muhammad Mamdani, Anthony Chang, Robert Hoyt, Ehsan Misaghi, Ying Wan, Gregg Gascon, James Barry, Johanna Kim, Mijanou Pham, Ryan McAdams, Harvey Castro, Shane Eaton
article en

Abstract

AI education in medicine remains fragmented, narrowly technical, and disconnected from healthcare realities. Through an international collaboration, we developed a framework spanning seven domains and 24 learning objectives. We reflect on what this process revealed: effective AI education must be clinically grounded, ethically integrated, and implementation-aware across the training continuum. We share key insights, tensions encountered, and lessons for educators in translating this framework into practice.

npj Digital Medicine
Dartmouth College (US), OhioHealth (US), American Board of Internal Medicine (US), Children's Hospital of Orange County (US), Cleveland Clinic (US), University of Wisconsin–Madison (US), University of Alberta (CA), Virginia Commonwealth University (US), University of Toronto (CA), Roseman University of Health Sciences (US), University of Cambridge (GB), North York General Hospital (CA), Association for the Advancement of Artificial Intelligence (US), University of California San Diego (US), Ted Rogers Centre for Heart Research (CA), Bambino Gesù Children's Hospital (IT), Mayo Clinic in Arizona (US), Public Health Ontario (CA), Artificial Intelligence in Medicine (Canada) (CA), Loma Linda University Children's Hospital (US), The University of Texas at San Antonio (US), New York University (US), University of Colorado Denver (US), Stanford University (US)
Partnerships for the goals
Openalex Percentile: Top 14%
Artificial Intelligence in Healthcare and Education
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