Radiology should be the medical specialty most primed to incorporate artificial intelligence
(AI) into our workflow; however, it seems that many of us are not even sure exactly
what that means. This disconnection can lead us to have a negative view of the future
of technology-enhanced radiology, which can then discourage medical students from
entering the field. Students rely on us to understand how radiology is incorporating
new technology and what the future of the field will look like for them, but many
of us are ill prepared to teach the younger generation about this, mostly because
we ourselves are not sure. The terms AI, machine learning (ML) and deep learning (DL)
are often used interchangeably, but these are vastly different concepts with varied
applications and potential. The purpose of the study by Gong et al (
1
) accurately captures the gap between the media hype surrounding AI in radiology and
the level of understanding, focusing on medical students’ perception. The key difference
in this article is the inclusion of additional questions to assess medical students’
knowledge level about AI, which elucidates the gap between perception and reality.To read this article in full you will need to make a payment
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References
- Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: a National Survey Study.Acad Radiol. 2019; 26: 566-577
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Data Science Institute, American College of Radiology. Available at: https://www.acrdsi.org. Accessed January 1, 2019.
- Data science and the future of radiology: a new pillar for JACR.J Am Coll Radiol. 2018; 15: 378
- “Canadian Association of Radiologists white paper on artificial intelligence in radiology.Can Assoc Radiol J. 2018; 69: 120-135
Article Info
Publication History
Published online: January 24, 2019
Accepted:
January 10,
2019
Received:
January 5,
2019
Footnotes
Invited guest editorial for article by Gong et al “Influence of Artificial Intelligence on Canadian Medical Students' Preference for Radiology Specialty: A National Survey Study.”
Identification
Copyright
© 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
