Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce
Abstract
The increasingly realistic prospect of artificial intelligence (AI) playing an important role in radiology has been welcomed with a mixture of enthusiasm and anxiousness. A consensus has arisen that AI will support radiologists in the interpretation of less challenging cases, which will give the radiologists more time to focus on the challenging tasks as well as interactions with patients and other clinicians. The possibility of AI replacing a large number of radiologists is generally dismissed by the radiology community. The common arguments include the following: (1) AI will never be able to match radiologists' performance; (2) radiologists do more than interpret images; (3) even if AI takes over a large portion of the reading tasks, the radiologists' effort will be shifted toward interactions with patients and other physicians; (4) the FDA would never agree to let machines do the work of radiologist; (5) the issues of legal liability would be insurmountable; and (6) patients would never put complete trust in computer algorithms. In this article, I analyze these arguments in detail. I find a certain level of validity to some of them. However, I conclude that none of the arguments provide sufficient support for the claim that AI will not create a significant disruption in the radiology workforce. Such disruption is a real possibility. Although the radiology specialty has shown an astonishing ability to adapt to the changing technology, the future is uncertain, and an honest, in-depth discussion is needed to guide development of the field.
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Evidence in the literature suggests that the first wave of AI applications is likely to constitute assistive technology, taking over repetitive tasks to improve consistency, such as reading radiographs.
“Evidence in the literature suggests that the first wave of AI applications is likely to constitute assistive technology, taking over repetitive tasks to improve consistency, such as reading radiographs 51 .”
From Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology (2020) · cites this paper as [51]
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- Authors
- Mazurowski, M. A.
- Affiliations
- Duke University (US)
- Corresponding author
- Mazurowski, Maciej A.
- Venue
- Journal of the American College of Radiology
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 16
- Issue
- 8
- Pages
- 1077-1082
- ISSN
- 1546-1440 (ISSN-L)
- OpenAlex
- W2936049295
- Cited by
- 95
- References
- 9
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 14 · 2023: 13 · 2022: 23 · 2021: 9 · 2020: 14 · 2019: 6
- FWCI
- 4.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American College of Radiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence in radiology, radiology workforce, job displacement, radiologist role, medical image interpretation, legal liability, patient trust in AI
- MeSH terms
- Workforce, Artificial Intelligence, Forecasting, Humans, Radiology
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