The fundamentals of Artificial Intelligence in medical education research: AMEE Guide No. 156

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.

Keywords:

• Artificial Intelligence
• machine learning
• big data
• medical education
• assessment

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

The author(s) reported there is no funding associated with the work featured in this article.

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.