Humans and machines: Moving towards a more symbiotic approach to learning clinical reasoning

Abstract

Artificial intelligence is a growing phenomenon that is driving major changes to how we deliver healthcare. One of its most significant and challenging contributions is likely to be in diagnosis. Artificial intelligence is challenging the physician’s exclusive role in diagnosis and in some areas, its diagnostic accuracy exceeds that of humans. We argue that we urgently need to consider how we will incorporate AI into our teaching of clinical reasoning in the undergraduate curriculum; students need to successfully navigate the benefits and potential issues of new and developing approaches to AI in clinical diagnosis. We offer a pedagogical framework for this challenging change to our curriculum.

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Additional information

Notes on contributors

Ralph Pinnock

Ralph Pinnock, MBChB, FRACP, MClinEd, Head Educational Unit, Dunedin School of Medicine, Otago University, Dunedin, New Zealand.

Jenny McDonald

Jenny McDonald, MBChB, PhD, Centre for Learning & Research in Higher Education (CLeaR), University of Auckland, Auckland, New Zealand.

Darren Ritchie

Darren Ritchie, Medical student Dunedin School of Medicine, Otago University, Dunedin New Zealand.

Steven J. Durning

Steven J. Durning, M.D., PhD, FACP, Director of Graduate Programs in Health Professions Education. Professor of medicine and pathology, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.