Abstract
Purpose
To report the results of a nationwide online survey on artificial intelligence (AI) among radiologist members of the Italian Society of Medical and Interventional Radiology (SIRM).
Methods and materials
All members were invited to the survey as an initiative by the Imaging Informatics Chapter of SIRM. The survey consisted of 13 questions about the participants’ demographic information, perceived advantages and issues related to AI implementation in radiological practice, and their overall opinion about AI.
Results
In total, 1032 radiologists (equaling 9.5% of active SIRM members for the year 2019) joined the survey. Perceived AI advantages included a lower diagnostic error rate (750/1027, 73.0%) and optimization of radiologists’ work (697/1027, 67.9%). The risk of a poorer professional reputation of radiologists compared with non-radiologists (617/1024, 60.3%), and increased costs and workload due to AI system maintenance and data analysis (399/1024, 39.0%) were seen as potential issues. Most radiologists stated that specific policies should regulate the use of AI (933/1032, 90.4%) and were not afraid of losing their job due to it (917/1032, 88.9%). Overall, 77.0% of respondents (794/1032) were favorable to the adoption of AI, whereas 18.0% (186/1032) were uncertain and 5.0% (52/1032) were unfavorable.
Conclusions
Radiologists had a mostly positive attitude toward the implementation of AI in their working practice. They were not concerned that AI will replace them, but rather that it might diminish their professional reputation.
Similar content being viewed by others
References
Yu KH, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nat Biomed Eng 2:719–731. https://doi.org/10.1038/s41551-018-0305-z
Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, Kadoury S, Tang A (2017) Deep learning: a primer for radiologists. Radiographics 37:2113–2131. https://doi.org/10.1148/rg.2017170077
Kohli M, Prevedello LM, Filice RW, Geis JR (2017) Implementing machine learning in radiology practice and research. AJR Am J Roentgenol 208:754–760. https://doi.org/10.2214/AJR.16.17224
European Society of Radiology (ESR) (2019) What the radiologist should know about artificial intelligence—an ESR white paper. Insights Imaging 10:44. https://doi.org/10.1186/s13244-019-0738-2
Pesapane F, Codari M, Sardanelli F (2018) Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2:35. https://doi.org/10.1186/s41747-018-0061-6
Coppola F, Bibbolino C, Grassi R, Pierotti L, Silverio R, Lassandro F, Neri E, Regge D (2016) Results of an Italian survey on teleradiology. Radiol Med 121(8):652–659. https://doi.org/10.1007/s11547-016-0640-7
Faggioni L, Coppola F, Ferrari R, Neri E, Regge D (2017) Usage of structured reporting in radiological practice: results from an Italian online survey. Eur Radiol 27:1934–1943. https://doi.org/10.1007/s00330-016-4553-6
Coppola F, Faggioni L, Grassi R, Bibbolino C, Rizzo A, Gandolfo N, Calvisi A, Cametti CA, Benea G, Giovagnoni A, Privitera C, Regge D (2019) Dematerialisation of patient’s informed consent in radiology: insights on current status and radiologists’ opinion from an Italian online survey. Radiol Med 124:846–853. https://doi.org/10.1007/s11547-019-01033-9
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 31(10):105. https://doi.org/10.1186/s13244-019-0798-3
van Hoek J, Huber A, Leichtle A, Härmä K, Hilt D, von Tengg-Kobligk H, Heverhagen J, Poellinger A (2019) A survey on the future of radiology among radiologists, medical students and surgeons: students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. Eur J Radiol 121:108742. https://doi.org/10.1016/j.ejrad.2019.108742
Goldberg JE, Rosenkrantz AB (2019) Artificial intelligence and radiology: a social media perspective. Curr Probl Diagn Radiol 48:308–311. https://doi.org/10.1067/j.cpradiol.2018.07.005
Waymel Q, Badr S, Demondion X, Cotten A, Jacques T (2019) Impact of the rise of artificial intelligence in radiology: what do radiologists think? Diagn Interv Imaging 100:327–336. https://doi.org/10.1016/j.diii.2019.03.015
Ooi SKG, Makmur A, Fook-Chong S, Liew C, Sia SY, Ting YH, Lim CY (2019) Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey. Singapore Med J. https://doi.org/10.11622/smedj.2019141
SFR-IA Group; CERF; French Radiology Community (2018) Artificial intelligence and medical imaging 2018: French Radiology Community white paper. Diagn Interv Imaging 99:727–742. https://doi.org/10.1016/j.diii.2018.10.003
Duong MT, Rauschecker AM, Rudie JD, Chen PH, Cook TS, Bryan RN, Mohan S (2019) Artificial intelligence for precision education in radiology. Br J Radiol 92:20190389. https://doi.org/10.1259/bjr.20190389
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510. https://doi.org/10.1038/s41568-018-0016-5
Kim TJ, Kim CH, Lee HY, Chung MJ, Shin SH, Lee KJ, Lee KS (2020) Management of incidental pulmonary nodules: current strategies and future perspectives. Expert Rev Respir Med 14:173–194. https://doi.org/10.1080/17476348.2020.1697853
Curtis C, Liu C, Bollerman TJ, Pianykh OS (2018) Machine learning for predicting patient wait times and appointment delays. J Am Coll Radiol 15:1310–1316. https://doi.org/10.1016/j.jacr.2017.08.021
Savadjiev P, Chong J, Dohan A, Vakalopoulou M, Reinhold C, Paragios N, Gallix B (2019) Demystification of AI-driven medical image interpretation: past, present and future. Eur Radiol 29:1616–1624. https://doi.org/10.1007/s00330-018-5674-x
Kobayashi Y, Ishibashi M, Kobayashi H (2019) How will “democratization of artificial intelligence” change the future of radiologists? Jpn J Radiol 37:9–14. https://doi.org/10.1007/s11604-018-0793-5
Mazurowski MA (2019) Artificial intelligence may cause a significant disruption to the radiology workforce. J Am Coll Radiol 16:1077–1082. https://doi.org/10.1016/j.jacr.2019.01.026
Sogani J, Allen B Jr, Dreyer K, McGinty G (2020) Artificial intelligence in radiology: the ecosystem essential to improving patient care. Clin Imaging 59:A3–A6. https://doi.org/10.1016/j.clinimag.2019.08.001
Jarrett D, Stride E, Vallis K, Gooding MJ (2019) Applications and limitations of machine learning in radiation oncology. Br J Radiol 92:20190001. https://doi.org/10.1259/bjr.20190001
Dilsizian SE, Siegel EL (2014) Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep 16:441. https://doi.org/10.1007/s11886-013-0441-8
Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T (2017) Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol 69:2657–2664. https://doi.org/10.1016/j.jacc.2017.03.571
Patel UK, Anwar A, Saleem S, Malik P, Rasul B, Patel K, Yao R, Seshadri A, Yousufuddin M, Arumaithurai K (2019) Artificial intelligence as an emerging technology in the current care of neurological disorders. J Neurol. https://doi.org/10.1007/s00415-019-09518-3
Gong B, Nugent JP, Guest W, Parker W, Chang PJ, Khosa F, Nicolaou S (2019) Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: a national survey study. Acad Radiol 26:566–577. https://doi.org/10.1016/j.acra.2018.10.007
Geis JR, Brady AP, Wu CC, Spencer J, Ranschaert E, Jaremko JL, Langer SG, Borondy Kitts A, Birch J, Shields WF et al (2019) Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Radiology 293:436–440. https://doi.org/10.1148/radiol.2019191586
Neri E, Coppola F, Miele V, Bibbolino C, Grassi R (2020) Artificial intelligence: who is responsible for the diagnosis? Radiol Med. https://doi.org/10.1007/s11547-020-01135-9
Eltorai AEM, Bratt AK, Guo HH (2019) Thoracic radiologists’ versus computer scientists’ perspectives on the future of artificial intelligence in radiology. J Thorac Imaging. https://doi.org/10.1097/RTI.0000000000000453
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies directly involving human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Coppola, F., Faggioni, L., Regge, D. et al. Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey. Radiol med 126, 63–71 (2021). https://doi.org/10.1007/s11547-020-01205-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11547-020-01205-y