Rationale and Objectives
Artificial intelligence (AI) has the potential to transform the clinical practice
of radiology. This study investigated Canadian medical students’ perceptions of the
impact of AI on radiology, and their influence on the students’ preference for radiology
specialty.
Materials and Methods
In March 2018, an anonymous online survey was distributed to students at all 17 Canadian
medical schools.
Results
Among 322 respondents, 70 students considered radiology as the top specialty choice,
and 133 as among the top three choices. Only a minority (29.3%) of respondents agreed
AI would replace radiologists in foreseeable future, but a majority (67.7%) agreed
AI would reduce the demand for radiologists. Even among first-choice respondents,
48.6% agreed AI caused anxiety when considering the radiology specialty. Furthermore,
one-sixth of respondents who would otherwise rank radiology as the first choice would
not consider radiology because of the anxiety about AI. Prior significant exposure
to radiology and high confidence in understanding of AI were shown to decrease the
anxiety level. Interested students valued the opinions of local radiologists, radiology
conferences, and journals. Students were most interested in “expert opinions on AI”
and “discussing AI in preclinical radiology lectures” to understand the impact of
AI.
Conclusion
Anxiety related to “displacement” (not “replacement”) of radiologists by AI discouraged
many medical students from considering the radiology specialty. The radiology community
should educate medical students about the potential impact of AI, to ensure radiology
is perceived as a viable long-term career choice.
Key Words
Abbreviations:
AI (Artificial Intelligence), CAR (Canadian Association of Radiologists), RSNA (Radiological Society of North America), CaRMS (Canadian Resident Matching Service), CACMS (Committee on Accreditation of Canadian Medical Schools), LCME (Liaison Committee on Medical Education)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: November 11, 2018
Accepted:
October 1,
2018
Received in revised form:
September 26,
2018
Received:
July 23,
2018
Footnotes
Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Identification
Copyright
© 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
