Radiology residents have time and time again recognized the importance of artificial
intelligence (AI) exposure in resident education and the need for hands-on experience
with AI (
1
,
- Salastekar N.V.
- Maxfield C.
- Hanna T.H.
- et al.
Artificial intelligence/machine learning education in radiology: multi-institutional
survey of radiology residents in the United States.
Acad Radiol. 2023; (S1076-6332(23)00005-3, Published Online: January 27)https://doi.org/10.1016/j.acra.2023.01.005
2
,
3
). Surveys of radiologists and radiologists-in-training have continually demonstrated
the gap between interest in AI and the accessibility of AI education resources. Salastekar
et al. (
1
) conducted the largest survey of US radiology residents to date, providing a broad
snapshot of US radiology residents’ perspectives on the need for and purpose of AI and
machine learning (ML) education in radiology training, reaching 209 residents from
21 programs throughout the United States. Keeping with the trends of several such
studies in the US and internationally, this survey demonstrated that an overwhelming
majority, 83% of respondents, agreed that AI should be a part of the radiology education
curriculum.- Salastekar N.V.
- Maxfield C.
- Hanna T.H.
- et al.
Artificial intelligence/machine learning education in radiology: multi-institutional
survey of radiology residents in the United States.
Acad Radiol. 2023; (S1076-6332(23)00005-3, Published Online: January 27)https://doi.org/10.1016/j.acra.2023.01.005
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References
- Artificial intelligence/machine learning education in radiology: multi-institutional survey of radiology residents in the United States.Acad Radiol. 2023; (S1076-6332(23)00005-3, Published Online: January 27)https://doi.org/10.1016/j.acra.2023.01.005
- 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/s0330-021-07781-5
- Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey.Singapore Med J. 2021; 62: 126-134
- Artificial intelligence literacy: developing a multi-institutional infrastructure for AI education.Acad Radiol. 2022; (S1076-6332(22)00516-5, Published Online: October 30)https://doi.org/10.1016/j.acra.2022.10.002
- An extensive survey of radiographers from the Middle East and India on artificial intelligence integration in radiology practice.Health Technol. 2021; 11: 1045-1050https://doi.org/10.1007/s12553-021-00583-1
- Artificial intelligence in low and middle-income countries: innovating global health radiology.Radiology. 2020; 297: 513-520https://doi.org/10.1148/radiol.2020201434
Artificial intelligence in radiology education. University of Alabama at Birmingham. Available at: https://sites.uab.edu/airad-ed. Accessed April 20, 2023.
Article info
Publication history
Published online: May 24, 2023
Accepted:
April 30,
2023
Received:
April 30,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved All rights reserved.
