The growth of cross-sectional imaging (particularly MR and CT) has contributed significantly
to increase the workload of radiologists, particularly due to larger datasets to interpret
in a shorter amount of time (
1
). In one institution, the number of cross-sectional images increased tenfold between
1990 and 2010 (
2
). This has led to increased intra- and inter-observer variability, burnout and diagnostic
errors. Against this backdrop, Artificial Intelligence (AI) may turn out to be an
attractive partner, one that can help in both interpretive and non-interpretive tasks
in radiology (
3
). Unfortunately, AI education has not reached many practicing radiologists, and without
specialized knowledge, they cannot either understand or explain, to both medical students
and radiology residents, how radiology is going to incorporate AI (
- Mello-Thoms C.
- Mello C.A.B.
Clinical applications of artificial intelligence in radiology.
Br J Radiol. 2023; https://doi.org/10.1259/bjr.20221031
4
), which propagates the fears in the student population that AI will take over radiologists’
jobs. In this context, AI training could be a protective factor against the disincentive
effect of AI on future radiologists (
5
), as it has been shown that fear of AI is directly linked to reduced knowledge about
it (
6
).To read this article in full you will need to make a payment
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References
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Article info
Publication history
Published online: May 20, 2023
Accepted:
March 13,
2023
Received in revised form:
March 1,
2023
Received:
December 17,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved All rights reserved.
