OBJECTIVES: Currently, hurdles to implementation of artificial intelligence (AI) in radiology are a much-debated topic but have not been investigated in the community at large. Also, controversy exists if and to what extent AI should be incorporated into radiology residency programs. METHODS: Between April and July 2019, an international survey took place on AI regarding its impact on the profession and training. The survey was accessible for radiologists and residents and distributed through several radiological societies. Relationships of independent variables with opinions, hurdles, and education were assessed using multivariable logistic regression. RESULTS: The survey was completed by 1041 respondents from 54 countries. A majority (n = 855, 82%) expects that AI will cause a change to the radiology field within 10 years. Most frequently, expected roles of AI in clinical practice were second reader (n = 829, 78%) and work-flow optimization (n = 802, 77%). Ethical and legal issues (n = 630, 62%) and lack of knowledge (n = 584, 57%) were mentioned most often as hurdles to implementation. Expert respondents added lack of labelled images and generalizability issues. A majority (n = 819, 79%) indicated that AI should be incorporated in residency programs, while less support for imaging informatics and AI as a subspecialty was found (n = 241, 23%). CONCLUSIONS: Broad community demand exists for incorporation of AI into residency programs. Based on the results of the current study, integration of AI education seems advisable for radiology residents, including issues related to data management, ethics, and legislation. KEY POINTS: • There is broad demand from the radiological community to incorporate AI into residency programs, but there is less support to recognize imaging informatics as a radiological subspecialty. • Ethical and legal issues and lack of knowledge are recognized as major bottlenecks for AI implementation by the radiological community, while the shortage in labeled data and IT-infrastructure issues are less often recognized as hurdles. • Integrating AI education in radiology curricula including technical aspects of data management, risk of bias, and ethical and legal issues may aid successful integration of AI into diagnostic radiology.

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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Huisman M, Ranschaert ER, Parker W et al (2021) An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. Eur Radiol. 10.1007/s00330-021-07781-5 PubMed PMC

Kotter E, Ranschaert E (2020) Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow. Eur Radiol 31:5–7 PubMed PMC

Strohm L, Hehakaya C, Ranschaert E, Boon W, Moors E (2020) Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. Eur Radiol 30(10):5525–5532 PubMed PMC

Geis JR, Brady AP, Wu CC, et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Can Assoc Radiol J. 2019;70:329–334. doi: 10.1016/j.carj.2019.08.010. PubMed DOI

Recht M, Dewey M, Dreyer K et al (2020) Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations. Eur Radiol 30(6):3576–3584 PubMed

Willemink MJ, Koszek WA, Hardell C, et al. Preparing medical imaging data for machine learning. Radiology. 2020;295:4–15. doi: 10.1148/radiol.2020192224. PubMed DOI PMC

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. PubMed DOI

Geis JR, Brady AP, Wu CC, et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Radiology. 2019;293:436–440. doi: 10.1148/radiol.2019191586. PubMed DOI

Kelley K, Clark B, Brown V, Sitzia J. Good practice in the conduct and reporting of survey research. International J Qual Health Care. 2003;15:261–266. doi: 10.1093/intqhc/mzg031. PubMed DOI

European Society of Radiology (ESR) (2019) Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights Imaging 10:105–103 PubMed PMC

Pinto Dos Santos D, Giese D, Brodehl S, et al. Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019;29:1640–1646. doi: 10.1007/s00330-018-5601-1. PubMed DOI

Oakden-Rayner L. Exploring large-scale public medical image datasets. Acad Radiol. 2019;27:106–112. doi: 10.1016/j.acra.2019.10.006. PubMed DOI

Thrall JH, Li X, Li Q, et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol. 2018;15:504–508. doi: 10.1016/j.jacr.2017.12.026. PubMed DOI