Data Science as a Core Competency in Undergraduate Medical Education in the Age of Artificial Intelligence in Health Care

Puneet Seth; Nancy Hueppchen; Steven D Miller; Frank Rudzicz; Jerry Ding; Kapil Parakh; Janet D Record

doi:10.2196/46344

Data Science as a Core Competency in Undergraduate Medical Education in the Age of Artificial Intelligence in Health Care

JMIR Med Educ. 2023 Jul 11:9:e46344. doi: 10.2196/46344.

Authors

Puneet Seth^#¹, Nancy Hueppchen^#², Steven D Miller^#³, Frank Rudzicz^#^{4

5

6}, Jerry Ding^#⁷, Kapil Parakh^#⁸, Janet D Record^#²

Affiliations

¹ Department of Family Medicine, McMaster University, Hamilton, ON, Canada.
² Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
³ Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
⁴ Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
⁵ Vector Institute for Artificial Intelligence, Toronto, ON, Canada.
⁶ Department of Computer Science, University of Toronto, Toronto, ON, Canada.
⁷ Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
⁸ Department of Medicine, Georgetown University, Washington, DC, United States.

^# Contributed equally.

PMID: 37432728
PMCID: PMC10369309
DOI: 10.2196/46344

Abstract

The increasingly sophisticated and rapidly evolving application of artificial intelligence in medicine is transforming how health care is delivered, highlighting a need for current and future physicians to develop basic competency in the data science that underlies this topic. Medical educators must consider how to incorporate central concepts in data science into their core curricula to train physicians of the future. Similar to how the advent of diagnostic imaging required the physician to understand, interpret, and explain the relevant results to patients, physicians of the future should be able to explain to patients the benefits and limitations of management plans guided by artificial intelligence. We outline major content domains and associated learning outcomes in data science applicable to medical student curricula, suggest ways to incorporate these themes into existing curricula, and note potential implementation barriers and solutions to optimize the integration of this content.

Keywords: AI; application; artificial intelligence; barriers; competency; data science; develop; education; health care; health care delivery; health data; integration; machine learning; medical education; medical educators; optimize; physician; training.

©Puneet Seth, Nancy Hueppchen, Steven D Miller, Frank Rudzicz, Jerry Ding, Kapil Parakh, Janet D Record. Originally published in JMIR Medical Education (https://mededu.jmir.org), 11.07.2023.