Machine Learning: The Next Paradigm Shift in Medical Education.

Journal: Academic medicine : journal of the Association of American Medical Colleges
PMID: 33496428

Abstract

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.

Authors

  • Cornelius A James
    C.A. James is assistant professor, Departments of Internal Medicine and Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan.
  • Kevin M Wheelock
    K.M. Wheelock is an internal medicine house officer, Yale School of Medicine, New Haven, Connecticut.
  • James O Woolliscroft
    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.

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