Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Sep;31(9):7058-7066.
doi: 10.1007/s00330-021-07781-5. Epub 2021 Mar 20.

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

Merel Huisman  1 , Erik Ranschaert  2 , William Parker  3 , Domenico Mastrodicasa  4 , Martin Koci  5 , Daniel Pinto de Santos  6 , Francesca Coppola  7 , Sergey Morozov  8 , Marc Zins  9 , Cedric Bohyn  10 , Ural Koç  11 , Jie Wu  12 , Satyam Veean  13 , Dominik Fleischmann  4 , Tim Leiner  14 , Martin J Willemink  4
Affiliations

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

Merel Huisman et al. Eur Radiol. 2021 Sep.

Abstract

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.

Keywords: Artificial intelligence; Diagnostic imaging; Radiology; Surveys and questionnaires.

PubMed Disclaimer

Conflict of interest statement

The authors of this manuscript declare relationships with the following companies: Segmed, Inc., Arterys, Quantib, Osimis.io.

Figures

Fig. 1
Fig. 1
Geographic heat map of survey respondents
See this image and copyright information in PMC

References

    1. Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digital Health. 2019;1:e271–e297. doi: 10.1016/S2589-7500(19)30123-2. - DOI - PubMed
    1. Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA. 2016;316:2353–2354. doi: 10.1001/jama.2016.17438. - DOI - PubMed
    1. Hinton G. Deep learning—a technology with the potential to transform health care. JAMA. 2018;320:1101–1102. doi: 10.1001/jama.2018.11100. - DOI - PubMed
    1. Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;368:m689. doi: 10.1136/bmj.m689. - DOI - PMC - PubMed
    1. Wichmann J, Willemink M, De Cecco C. Artificial intelligence and machine learning in radiology: current state and considerations for routine clinical implementation. Invest Radiol. 2020;55:619–627. doi: 10.1097/RLI.0000000000000673. - DOI - PubMed

LinkOut - more resources