Universität zu Köln

Huisman, Merel, Ranschaert, Erik, Parker, William, Mastrodicasa, Domenico ORCID: 0000-0001-8227-0757, Koci, Martin, de Santos, Daniel Pinto, Coppola, Francesca ORCID: 0000-0001-8957-4606, Morozov, Sergey, Zins, Marc, Bohyn, Cedric, Koc, Ural, Wu, Jie, Veean, Satyam, Fleischmann, Dominik, Leiner, Tim and Willemink, Martin J. (2021). An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education. Eur. Radiol., 31 (11). S. 8797 - 8807. NEW YORK: SPRINGER. ISSN 1432-1084

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Abstract

Objectives Currently, hurdles to implementation of artificial intelligence (AI) in radiology are a much-debated topic but have not been investigated in the community at large. Also, controversy exists if and to what extent AI should be incorporated into radiology residency programs. Methods Between April and July 2019, an international survey took place on AI regarding its impact on the profession and training. The survey was accessible for radiologists and residents and distributed through several radiological societies. Relationships of independent variables with opinions, hurdles, and education were assessed using multivariable logistic regression. Results The survey was completed by 1041 respondents from 54 countries. A majority (n = 855, 82%) expects that AI will cause a change to the radiology field within 10 years. Most frequently, expected roles of AI in clinical practice were second reader (n = 829, 78%) and work-flow optimization (n = 802, 77%). Ethical and legal issues (n = 630, 62%) and lack of knowledge (n = 584, 57%) were mentioned most often as hurdles to implementation. Expert respondents added lack of labelled images and generalizability issues. A majority (n = 819, 79%) indicated that AI should be incorporated in residency programs, while less support for imaging informatics and AI as a subspecialty was found (n = 241, 23%). Conclusions Broad community demand exists for incorporation of AI into residency programs. Based on the results of the current study, integration of AI education seems advisable for radiology residents, including issues related to data management, ethics, and legislation.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Huisman, Merel UNSPECIFIED UNSPECIFIED UNSPECIFIED Ranschaert, Erik UNSPECIFIED UNSPECIFIED UNSPECIFIED Parker, William UNSPECIFIED UNSPECIFIED UNSPECIFIED Mastrodicasa, Domenico UNSPECIFIED orcid.org/0000-0001-8227-0757 UNSPECIFIED Koci, Martin UNSPECIFIED UNSPECIFIED UNSPECIFIED de Santos, Daniel Pinto UNSPECIFIED UNSPECIFIED UNSPECIFIED Coppola, Francesca UNSPECIFIED orcid.org/0000-0001-8957-4606 UNSPECIFIED Morozov, Sergey UNSPECIFIED UNSPECIFIED UNSPECIFIED Zins, Marc UNSPECIFIED UNSPECIFIED UNSPECIFIED Bohyn, Cedric UNSPECIFIED UNSPECIFIED UNSPECIFIED Koc, Ural UNSPECIFIED UNSPECIFIED UNSPECIFIED Wu, Jie UNSPECIFIED UNSPECIFIED UNSPECIFIED Veean, Satyam UNSPECIFIED UNSPECIFIED UNSPECIFIED Fleischmann, Dominik UNSPECIFIED UNSPECIFIED UNSPECIFIED Leiner, Tim UNSPECIFIED UNSPECIFIED UNSPECIFIED Willemink, Martin J. UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-602387
DOI:	10.1007/s00330-021-07782-4
Journal or Publication Title:	Eur. Radiol.
Volume:	31
Number:	11
Page Range:	S. 8797 - 8807
Date:	2021
Publisher:	SPRINGER
Place of Publication:	NEW YORK
ISSN:	1432-1084
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language ARTIFICIAL-INTELLIGENCE Multiple languages Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/60238