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

Merel Huisman,Sergey Morozov,Martin J Willemink,Satyam Veean,Dominik Fleischmann,Daniel Pinto De Santos,Marc Zins,Erik Ranschaert,Cedric Bohyn,Martin Koci,Tim Leiner,William Parker,Domenico Mastrodicasa,Jie Wu,Francesca Coppola,Ural Koç

doi:10.1007/s00330-021-07781-5

Abstract

ObjectivesRadiologists’ 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.MethodsBetween 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.ResultsThe 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).ConclusionsLimited 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.

Highlights

Artificial intelligence (AI) and deep learning (DL) algorithms have shown a promising performance when applied to medical imaging [1,2,3,4]
Radiologists and residents with basic knowledge levels, on the other hand, had a significantly less open and proactive attitude towards artificial intelligence (AI). This may indicate that increased AI-specific knowledge enhances adoption of AI in clinical practice, while basic knowledge levels may be inhibitive
Fear of replacement by AI still exists in the radiology community, as this was reported by 39% (n = 401)

Summary

Introduction

Artificial intelligence (AI) and deep learning (DL) algorithms have shown a promising performance when applied to medical imaging [1,2,3,4]. Radiologists and residents with an open and proactive attitude (i.e., those who are willing to invest extra time in AI in an already full clinical schedule) can be considered early adopters. These proactive physicians are needed to drive the phase so that the early majority will start using the tools and a tipping point can be reached [11]. This is crucial, because this will enable thorough validation of AI tools in clinical practice while feedback of the enduser is generated. Adoption of AI by radiologists may prevent the dreaded scenario that data is used for financial reasons rather than improvement of patient care [12]

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