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.

Department of Civil and Environmental Engineering Stanford University Stanford CA USA

Department of Health Care of Moscow Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies Moscow Russia

Department of Medical Imaging Saint Joseph Hospital Paris France

Department of Radiology Elisabeth TweeSteden Ziekenhuis Tilburg The Netherlands

Department of Radiology IRCCS Azienda Ospedaliero Universitaria di Bologna Bologna Italy

Department of Radiology Motol University Hospital Prague Czech Republic

Department of Radiology Stanford University School of Medicine Stanford CA USA

Department of Radiology University Hospital of Cologne Cologne Germany

Department of Radiology University Medical Center Utrecht Utrecht The Netherlands

Department of Radiology University of British Columbia Vancouver Canada

Department of Radiology UT Southwestern Medical Center Dallas TX USA

Department of Radiology UZ Leuven Leuven Belgium

Section of Radiology Ankara Golbasi Sehit Ahmet Ozsoy State Hospital Ankara Turkey

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An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education