Machine Learning
The Next Paradigm Shift in Medical Education
Bibliographic Data
| ID | 21612759 |
|---|---|
| Authors | Cornelius A James (0000-0002-6056-0582, C.A. Jamesis assistant professor, Departments of Internal Medicine and Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan., corresponding author), Kevin M Wheelock (K.M. Wheelockis an internal medicine house officer, Yale School of Medicine, New Haven, Connecticut.), Kevin Wheelock (0000-0002-5181-6494, Yale University), James O Woolliscroft (0000-0002-5756-9041, J.O. Woolliscroftis professor, Departments of Internal Medicine and Learning Health Sciences, and Lyle C. Roll Professor of Medicine, University of Michigan Medical School, Ann Arbor, Michigan.) |
| Year | 2021 |
| Volume | 96 |
| Issue | 7 |
| Pages | 954-957 |
| Publication date | 2021-07-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Academic Medicine (JOURNAL) |
| Journal identifiers | ISSN: 1040-2446 • E-ISSN: 1938-808X |
| Publisher | Oxford University Press (OUP) (PUBLISHER) |
| DOI | 10.1097/acm.0000000000003943 |
| PMID | 33496428 |
| OpenAlex | W3125170355 |
| Language | EN |
| Citations received | 7 |
| References cited | 10 |
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
Alternative medicine · Clinical Practice · Curriculum · Engineering ethics · Evidence-based medicine · Health care · Medical education · MEDLINE · Paradigm shift · Political science · Position paper · Software deployment · Artificial Intelligence in Healthcare and Education · Computer Science · Electronic Health Records Systems · Engineering · Healthcare cost, quality, practices · Medicine · Nursing · Psychology
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| Unique citing works | 7 |
|---|---|
| Citations per year | 1,75 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 7 |