Artificial Intelligence: Threat or Boon to Radiologists?
Abstract
The development and integration of machine learning/artificial intelligence into routine clinical practice will significantly alter the current practice of radiology. Changes in reimbursement and practice patterns will also continue to affect radiology. But rather than being a significant threat to radiologists, we believe these changes, particularly machine learning/artificial intelligence, will be a boon to radiologists by increasing their value, efficiency, accuracy, and personal satisfaction.
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A great debate has been unfolding as to the impact of AI tools on professionals in radiology and how AI may entirely replace the radiology profession.
“Recently, a great debate has been unfolding as to the impact of AI tools on professionals in this field and how AI may entirely replace the radiology profession (Grady 2019, Mukherjee 2017, Recht and Bryan 2017).”
From To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis (2022) · cites this paper as Recht and Bryan 2017
AI can enhance both the value and the professional satisfaction of sonographers and maternal-fetal medicine experts by reducing the time needed for routine tasks and allowing more time to perform tasks that add value and influence patient care.
“AI can enhance both the value and the professional satisfaction of sonographers and maternal–fetal medicine experts by reducing the time needed for routine tasks and allowing more time to perform tasks that add value and influence patient care 49 , 50 .”
From Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology (2020) · cites this paper as [50]
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- Authors
- Recht, M., & Bryan, R. N.
- Affiliations
- NYU Langone Health (US)
- The University of Texas at Austin (US)
- Corresponding author
- Recht, Michael
- Venue
- Journal of the American College of Radiology
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 14
- Issue
- 11
- Pages
- 1476-1480
- ISSN
- 1546-1440 (ISSN-L)
- OpenAlex
- W2745394387
- Cited by
- 172
- References
- 9
- Citations by year
- 2026: 13 · 2025: 14 · 2024: 22 · 2023: 24 · 2022: 25 · 2021: 20 · 2020: 22 · 2019: 17 · 2018: 14 · 2017: 1
- FWCI
- 8.60
- 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
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence, machine learning, radiology practice, radiologist role, clinical practice patterns, reimbursement, practice efficiency, diagnostic accuracy, job satisfaction
- MeSH terms
- Radiologists, Artificial Intelligence, Clinical Competence, Efficiency, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Personal Satisfaction, Practice Patterns, Physicians', Reimbursement Mechanisms
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