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Attitudes Toward Artificial Intelligence Among Radiologists, IT Specialists, and Industry

Authors :
Christoph Düber
Peter Mildenberger
Tobias Jorg
Felix Hahn
Stefanie M. Jungmann
Daniel Pinto dos Santos
Florian Jungmann
Roman Kloeckner
Source :
Academic Radiology.
Publication Year :
2020
Publisher :
Elsevier BV, 2020.

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.

Details

ISSN :
10766332
Database :
OpenAIRE
Journal :
Academic Radiology
Accession number :
edsair.doi.dedup.....2e62be469c3e7bb0d7b6f4540f204e4a
Full Text :
https://doi.org/10.1016/j.acra.2020.04.011