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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References
- Canadian association of radiologists white paper on artificial intelligence in radiology.Can Assoc Radiol J. 2018; 69: 120-135
- Deep learning in radiology.Acad Radiol. 2018; 25: 1472-1480
- Current applications and future impact of machine learning in radiology.Radiology. 2018; 288: 318-328
- The future of radiology augmented with artificial intelligence: a strategy for success.Eur J Radiol. 2018; 102: 152-156
- Artificial intelligence: threat or boon to radiologists?.J Am Coll Radiol. 2017; 14: 1476-1480
- The ACR Data Science Institute and AI Advisory Group: harnessing the power of artificial intelligence to improve patient care.J Am Coll Radiol. 2018; 15: 577-579
- From images to actions: opportunities for artificial intelligence in radiology.Radiology. 2017; 285: 719-720
- A brave new world without diagnostic radiologists? Really?.Can Assoc Radiol J. 2018; 69: 119
- A.I. versus M.D.The New Yorker. Condé Nast, New York City, NY, United States2017
Why scan-reading artificial intelligence is bad news for radiologists. The Economist. London, England. 2017.
- Opinion: rise of the robot radiologists.The Scientist. LabX Media Group, New York City, United States2018
- Artificial intelligence and the practice of radiology: an alternative view.J Am Coll Radiol. 2018; 15: 1004-1007
- Predicting the future—big data, machine learning, and clinical medicine.N Engl J Med. 2016; 375: 1216-1219
- The end of radiology? Three threats to the future practice of radiology.J Am Coll Radiol. 2016; 13: 1415-1420
- Video from RSNA 2016: AI and radiology—separating hope from hype.http://www.youtube.comDate: 2016
- Cutting through the AI hype B.S.: Drs. Eliot Siegel, Rasu Shrestha, Greg Moore and Mr. Elad Benjamin talk AI and the future of radiology.www.medium.comDate: 2017
- Medical students are our future.J Am Coll Radiol. 2005; 2: 795-797
- Rest assured, João, you are safe from artificial intelligence.Appl Radiol. 2017; 46: 4-8
- How will AI affect radiology over the next 20years?.http://www.auntminnie.comDate: 2017
- Radiology leaders must counter hype over AI in radiology.http://www.auntminnie.comDate: 2018
- Medical students' attitude towards artificial intelligence: a multicentre survey.Eur Radiol. 2018;
- 2017 Canadian medical education statistics, Ottawa2017
- R-1 match interactive data.http://www.carms.caDate: 2018
- Deep learning: a primer for radiologists.Radiographics. 2017; 37: 2113-2131
- RSNA to publish three new journals.http://www.rsna.orgDate: 2017
- Artificial intelligence.http://www.car.caDate: 2018
- AI resources.http://www.car.caDate: 2018
- RSNA 2017: Rads who use AI will replace rads who don't.http://aimi.stanford.eduDate: 2017
- Surveying fourth-year medical students regarding the choice of diagnostic radiology as a specialty.J Am Coll Radiol. 2016; 13: 188-1895
- Educational impact of a structured radiation oncology clerkship curriculum: an interinstitutional comparison.J Am Coll Radiol. 2017; 14: 96-102
- Motivation and academic performance of medical students from ethnic minorities and majority: a comparative study.BMC Med Educ. 2017; 17: 233
- Vaccine education of medical students: a nationwide cross-sectional survey.Am J Prev Med. 2017; 53: e97-e104
- Procedural skills of the entrustable professional activities: are graduating US medical students prepared to perform procedures in residency?.J Surg Educ. 2017; 74: 589-595
- Mail surveys for election forecasting? An evaluation of the colombia dispatch poll.Public Opin Q. 1996; 60: 181-227
- Gauging the impact of growing nonresponse on estimates from a national RDD telephone survey.Public Opin Q. 2006; 70: 759-779
- The radiology report as seen by radiologists and referring clinicians: results of the COVER and ROVER surveys.Radiology. 2011; 259: 184-195
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.
