Abstract
The use of Artificial Intelligence (AI) in medical education has the potential to facilitate complicated tasks and improve efficiency. For example, AI could help automate assessment of written responses, or provide feedback on medical image interpretations with excellent reliability. While applications of AI in learning, instruction, and assessment are growing, further exploration is still required. There exist few conceptual or methodological guides for medical educators wishing to evaluate or engage in AI research. In this guide, we aim to: 1) describe practical considerations involved in reading and conducting studies in medical education using AI, 2) define basic terminology and 3) identify which medical education problems and data are ideally-suited for using AI.
Author contributors
The authors are all medical education scientists with a special interest in quantitative methods. They have contributed with guides and conceptual papers involving advanced statistical methods and the use of machine learning/artificial intelligence in medical education.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
Martin G. Tolsgaard
Martin G. Tolsgaard, MD, PhD, DMSc, is a professor of medical education at CAMES, University of Copenhagen, Denmark. His current research focuses on technology-enhanced medical education.
Martin V. Pusic
Martin V. Pusic, MD PhD, is professor of medical education, Department of Pediatrics, Harvard University, Boston, MA, USA. His current research focuses on learner analytics.
Stefanie S. Sebok-Syer
Stefanie S. Sebok-Syer, PhD, is assistant professor in medical education, Department of Emergency Medicine, Stanford University, Palo Alto, CA, USA. Her current research focuses on assessment of learners.
Brian Gin
Brian Gin, MD, is a researcher in medical education, Department of Pediatrics, University of California San Francisco, San Francisco, USA. His current work involves AI in medical education.
Morten Bo Svendsen
Morten Bo Svendsen, PhD, is an associate professor at the University of Copenhagen. His research involves technology-augmented medical education.
Mark D. Syer
Mark D. Syer, PhD, is a data scientist affiliated with Queen’s University, Kingston, Canada. His work involves the use of AI and advanced statistical modelling.
Ryan Brydges
Ryan Brydges, PhD, is a professor of medical education, University of Toronto, Toronto, Canada. His work involves self-regulated learning.
Monica M. Cuddy
Monica M. Cuddy is a measurement specialist at NBME, Philadelphia, PA, USA. Her work involves assessment of learners in health professions education.
Christy K. Boscardin
Christy K. Boscardin is a professor of medical education, Department of Medicine and Anesthesia, University of California San Francisco, San Francisco, CA. Her current research focuses on assessment of medical education.