Literature DB >> 30975611

Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce.

Maciej A Mazurowski1.   

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.
Copyright © 2019. Published by Elsevier Inc.

Entities:  

Keywords:  Artificial intelligence; future of radiology; machine learning; opinion

Mesh:

Year:  2019        PMID: 30975611     DOI: 10.1016/j.jacr.2019.01.026

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  15 in total

Review 1.  Current applications and future directions of deep learning in musculoskeletal radiology.

Authors:  Pauley Chea; Jacob C Mandell
Journal:  Skeletal Radiol       Date:  2019-08-04       Impact factor: 2.199

2.  Do We Expect More from Radiology AI than from Radiologists?

Authors:  Maciej A Mazurowski
Journal:  Radiol Artif Intell       Date:  2021-03-17

3.  Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.

Authors:  Francesca Coppola; Lorenzo Faggioni; Daniele Regge; Andrea Giovagnoni; Rita Golfieri; Corrado Bibbolino; Vittorio Miele; Emanuele Neri; Roberto Grassi
Journal:  Radiol Med       Date:  2020-04-29       Impact factor: 3.469

Review 4.  Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.

Authors:  Ling Yang; Ioana Cezara Ene; Reza Arabi Belaghi; David Koff; Nina Stein; Pasqualina Lina Santaguida
Journal:  Eur Radiol       Date:  2021-09-21       Impact factor: 5.315

5.  Who Will Get Paid for Artificial Intelligence in Medicine?

Authors:  Colin Rowell; Ronnie Sebro
Journal:  Radiol Artif Intell       Date:  2022-08-03

6.  Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review.

Authors:  Albert Boonstra; Mente Laven
Journal:  BMC Health Serv Res       Date:  2022-05-18       Impact factor: 2.908

7.  Promises of artificial intelligence in neuroradiology: a systematic technographic review.

Authors:  Allard W Olthof; Peter M A van Ooijen; Mohammad H Rezazade Mehrizi
Journal:  Neuroradiology       Date:  2020-04-22       Impact factor: 2.804

8.  Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors.

Authors:  Lea Strohm; Charisma Hehakaya; Erik R Ranschaert; Wouter P C Boon; Ellen H M Moors
Journal:  Eur Radiol       Date:  2020-05-26       Impact factor: 5.315

Review 9.  Virtual clinical trials in medical imaging: a review.

Authors:  Ehsan Abadi; William P Segars; Benjamin M W Tsui; Paul E Kinahan; Nick Bottenus; Alejandro F Frangi; Andrew Maidment; Joseph Lo; Ehsan Samei
Journal:  J Med Imaging (Bellingham)       Date:  2020-04-11

Review 10.  Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.

Authors:  Hossein Arabi; Habib Zaidi
Journal:  Eur J Hybrid Imaging       Date:  2020-09-23
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