Universität zu Köln

Jungmann, Florian, Jorg, Tobias, Hahn, Felix ORCID: 0000-0001-5122-9014, dos Santos, Daniel Pinto, Jungmann, Stefanie Maria, Duber, Christoph, Mildenberger, Peter and Kloeckner, Roman ORCID: 0000-0001-5492-4792 (2021). Attitudes Toward Artificial Intelligence Among Radiologists, IT Specialists, and Industry. Acad. Radiol., 28 (6). S. 834 - 841. NEW YORK: ELSEVIER SCIENCE INC. ISSN 1878-4046

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Abstract

Objectives: We investigated the attitudes of radiologists, information technology (IT) specialists, and industry representatives on artificial intelligence (AI) and its future impact on radiological work. Materials and Methods: During a national meeting for AI, eHealth, and IT infrastructure in 2019, we conducted a survey to obtain participants' attitudes. A total of 123 participants completed 28 items exploring AI usage in medicine. The Kruskal-Wallis test was used to identify differences between radiologists, IT specialists, and industry representatives. Results: The strongest agreement between all respondents occurred with the following: plausibility checks are important to understand the decisions of the AI (93% agreement), validation of AI algorithms is mandatory (91%), and medicine becomes more efficient in the age of AI (86%). In contrast, only 25% of the respondents had confidence in the AI results, and only 17% believed that medicine will become more human through the use of AI. The answers were significantly different between the three professions for four items: relevance for protocol selection in cross-sectional imaging (p = 0.034), medical societies should be involved in validation (p = 0.028), patients should be informed about the use of AI (p = 0.047), and AI should be part of medical education (p = 0.026). Conclusion: Currently, a discrepancy exists between high expectations for the future role of AI and low confidence in the results. This attitude was similar across all three groups. The demand for plausibility checks and the need to prove the usefulness in randomized controlled studies indicate what is needed in future research.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Jungmann, Florian UNSPECIFIED UNSPECIFIED UNSPECIFIED Jorg, Tobias UNSPECIFIED UNSPECIFIED UNSPECIFIED Hahn, Felix UNSPECIFIED orcid.org/0000-0001-5122-9014 UNSPECIFIED dos Santos, Daniel Pinto UNSPECIFIED UNSPECIFIED UNSPECIFIED Jungmann, Stefanie Maria UNSPECIFIED UNSPECIFIED UNSPECIFIED Duber, Christoph UNSPECIFIED UNSPECIFIED UNSPECIFIED Mildenberger, Peter UNSPECIFIED UNSPECIFIED UNSPECIFIED Kloeckner, Roman UNSPECIFIED orcid.org/0000-0001-5492-4792 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-572990
DOI:	10.1016/j.acra.2020.04.011
Journal or Publication Title:	Acad. Radiol.
Volume:	28
Number:	6
Page Range:	S. 834 - 841
Date:	2021
Publisher:	ELSEVIER SCIENCE INC
Place of Publication:	NEW YORK
ISSN:	1878-4046
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language PERFORMANCE; MEDICINE; CARE Multiple languages Radiology, Nuclear Medicine & Medical Imaging Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/57299