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 (
- Kwee T.C.
- Kwee R.M.
Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and the role of artificial intelligence.
Insights Imaging. 2021; 12: 88https://doi.org/10.1186/s13244-021-01031-4
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 (
- McDonald R.J.
- Schwartz K.M.
- Eckel L.J.
- et al.
The effects of change in utilization and technological advancements of cross-sectional imaging on radiologist workload.
Acad Radiol. 2015; 22: 1191-1198https://doi.org/10.1016/j.acra.2015.05.007
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 (
- Grayev A.
Artificial intelligence in radiology: resident recruitment help or hindrance.
Acad Radiol. 2019; 26: 699-700https://doi.org/10.1016/j.acra.2019.01.005
5), as it has been shown that fear of AI is directly linked to reduced knowledge about it (
- Gorospe-Sarasúa L.
- Muñoz-Olmedo J.M.
- Sendra-Portero F.
- et al.
Challenges in radiology education in the era of artificial intelligence.
Radiología. 2022; 64: 54-59https://doi.org/10.1016/j.rxeng.2020.10.012
- Huisman M.
- Ranschaert E.
- Parker W.
- et al.
An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude.
Eur Radiol. 2021; 31: 7058-7066https://doi.org/10.1007/s00330-021-07781-5
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- Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and the role of artificial intelligence.Insights Imaging. 2021; 12: 88https://doi.org/10.1186/s13244-021-01031-4
- The effects of change in utilization and technological advancements of cross-sectional imaging on radiologist workload.Acad Radiol. 2015; 22: 1191-1198https://doi.org/10.1016/j.acra.2015.05.007
- Clinical applications of artificial intelligence in radiology.Br J Radiol. 2023; https://doi.org/10.1259/bjr.20221031
- Artificial intelligence in radiology: resident recruitment help or hindrance.Acad Radiol. 2019; 26: 699-700https://doi.org/10.1016/j.acra.2019.01.005
- Challenges in radiology education in the era of artificial intelligence.Radiología. 2022; 64: 54-59https://doi.org/10.1016/j.rxeng.2020.10.012
- An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude.Eur Radiol. 2021; 31: 7058-7066https://doi.org/10.1007/s00330-021-07781-5
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- An artificial intelligence training workshop for diagnostic radiology residents.Radiol Artif Intell. 2023; 5e220170https://doi.org/10.1148/ryai.220170
- What should radiology residency and fellowship training in artificial intelligence include? A trainee’s perspective – radiology in training.Radiology. 2021; 299: E243-E245https://doi.org/10.1148/radiol.2021204406
- AI-RADS: an artificial intelligence curriculum for residents.Acad Radiol. 2021; 28: 1810-1816https://doi.org/10.1016/j.acra.2020.09.017
- A conference-friendly, hands-on introduction to deep learning for radiology trainees.J Digit Imaging. 2021; 34: 1026-1033https://doi.org/10.1007/s10278.021.00492-9
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Published online: May 20, 2023
Accepted: March 13, 2023
Received in revised form: March 1, 2023
Received: December 17, 2022
Publication stageIn Press Corrected Proof
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved All rights reserved.