Machine Learning: The Next Paradigm Shift in Medical Education.

Acad Med

J.O. Woolliscroft is professor, Departments of Internal Medicine and Learning Health Sciences, and Lyle C. Roll Professor of Medicine, University of Michigan Medical School, Ann Arbor, Michigan.

Published: July 2021

Machine learning (ML) algorithms are powerful prediction tools with immense potential in the clinical setting. There are a number of existing clinical tools that use ML, and many more are in development. Physicians are important stakeholders in the health care system, but most are not equipped to make informed decisions regarding deployment and application of ML technologies in patient care. It is of paramount importance that ML concepts are integrated into medical curricula to position physicians to become informed consumers of the emerging tools employing ML. This paradigm shift is similar to the evidence-based medicine (EBM) movement of the 1990s. At that time, EBM was a novel concept; now, EBM is considered an essential component of medical curricula and critical to the provision of high-quality patient care. ML has the potential to have a similar, if not greater, impact on the practice of medicine. As this technology continues its inexorable march forward, educators must continue to evaluate medical curricula to ensure that physicians are trained to be informed stakeholders in the health care of tomorrow.

Download full-text PDF

Source
http://dx.doi.org/10.1097/ACM.0000000000003943DOI Listing

Publication Analysis

Top Keywords

medical curricula
12
machine learning
8
paradigm shift
8
stakeholders health
8
health care
8
patient care
8
learning paradigm
4
medical
4
shift medical
4
medical education
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!