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An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude

M. Huisman, E. Ranschaert, W. Parker, D. Mastrodicasa, M. Koci, D. Pinto de Santos, F. Coppola, S. Morozov, M. Zins, C. Bohyn, U. Koç, J. Wu, S. Veean, D. Fleischmann, T. Leiner, MJ. Willemink

. 2021 ; 31 (9) : 7058-7066. [pub] 20210320

Language English Country Germany

Document type Journal Article

Persistent link   https://www.medvik.cz/link/bmc21025020

OBJECTIVES: Radiologists' perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond. METHODS: Between April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression. RESULTS: The survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24-74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10-2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20-0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21-0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25-31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16-50.54, p < 0.001). CONCLUSIONS: Limited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption. KEY POINTS: • Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI. • Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive. • AI should be incorporated in radiology training curricula to help facilitate its clinical adoption.

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$a Huisman, Merel $u Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands. merel.huisman1@gmail.com
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$a OBJECTIVES: Radiologists' perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond. METHODS: Between April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression. RESULTS: The survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24-74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10-2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20-0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21-0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25-31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16-50.54, p < 0.001). CONCLUSIONS: Limited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption. KEY POINTS: • Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI. • Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive. • AI should be incorporated in radiology training curricula to help facilitate its clinical adoption.
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$a Ranschaert, Erik $u Department of Radiology, Elisabeth-TweeSteden Ziekenhuis, Tilburg, The Netherlands
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$a Parker, William $u Department of Radiology, University of British Columbia, Vancouver, Canada
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$a Mastrodicasa, Domenico $u Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
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$a Koci, Martin $u Department of Radiology, Motol University Hospital, Prague, Czech Republic
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$a Pinto de Santos, Daniel $u Department of Radiology, University Hospital of Cologne, Cologne, Germany
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$a Coppola, Francesca $u Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
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$a Morozov, Sergey $u Department of Health Care of Moscow, Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Moscow, Russia
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$a Zins, Marc $u Department of Medical Imaging, Saint Joseph Hospital, Paris, France
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$a Bohyn, Cedric $u Department of Radiology, UZ Leuven, Leuven, Belgium
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$a Koç, Ural $u Section of Radiology, Ankara Golbasi Sehit Ahmet Ozsoy State Hospital, Ankara, Turkey
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$a Wu, Jie $u Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA
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$a Veean, Satyam $u Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
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$a Fleischmann, Dominik $u Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
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$a Leiner, Tim $u Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
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$a Willemink, Martin J $u Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
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