Impact of AI on radiology: a EuroAIM/EuSoMII 2024 survey among members of the European Society of Radiology

被引:3
作者
Zanardo, Moreno [1 ]
Visser, Jacob J. [2 ]
Colarieti, Anna [1 ]
Cuocolo, Renato [3 ]
Klontzas, Michail E. [4 ,5 ,6 ]
dos Santos, Daniel Pinto [7 ,8 ]
Sardanelli, Francesco [9 ]
机构
[1] IRCCS Policlin San Donato, Unit Radiol, San Donato Milanese, Italy
[2] Erasmus MC, Dept Radiol & Nucl Med, Rotterdam, Netherlands
[3] Univ Salerno, Dept Med Surg & Dent, Baronissi, Italy
[4] Univ Crete, Sch Med, Dept Radiol, Iraklion, Greece
[5] Karolinska Inst, Dept Clin Sci Intervent & Technol CLINTEC, Div Radiol, Stockholm, Sweden
[6] Fdn Res & Technol Hellas, Inst Comp Sci, Computat Biomed Lab, Iraklion, Crete, Greece
[7] Univ Hosp Frankfurt, Dept Radiol, Frankfurt, Germany
[8] Univ Hosp Cologne, Dept Radiol, Cologne, Germany
[9] Milano Monza Brianza, Lega Italiana Lotta Contro & Tumori LILT, Milan, Italy
来源
INSIGHTS INTO IMAGING | 2024年 / 15卷 / 01期
关键词
Artificial intelligence; Radiology; Diagnostic imaging; Surveys and questionnaires; IMAGES;
D O I
10.1186/s13244-024-01801-w
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
In order to assess the perceptions and expectations of the radiology staff about artificial intelligence (AI), we conducted an online survey among ESR members (January-March 2024). It was designed considering that conducted in 2018, updated according to recent advancements and emerging topics, consisting of seven questions regarding demographics and professional background and 28 AI questions. Of 28,000 members contacted, 572 (2%) completed the survey. AI impact was predominantly expected on breast and oncologic imaging, primarily involving CT, mammography, and MRI, and in the detection of abnormalities in asymptomatic subjects. About half of responders did not foresee an impact of AI on job opportunities. For 273/572 respondents (48%), AI-only reports would not be accepted by patients; and 242/572 respondents (42%) think that the use of AI systems will not change the relationship between the radiological team and the patient. According to 255/572 respondents (45%), radiologists will take responsibility for any AI output that may influence clinical decision-making. Of 572 respondents, 274 (48%) are currently using AI, 153 (27%) are not, and 145 (25%) are planning to do so. In conclusion, ESR members declare familiarity with AI technologies, as well as recognition of their potential benefits and challenges. Compared to the 2018 survey, the perception of AI's impact on job opportunities is in general slightly less optimistic (more positive from AI users/researchers), while the radiologist's responsibility for AI outputs is confirmed. The use of large language models is declared not only limited to research, highlighting the need for education in AI and its regulations.Critical relevance statementThis study critically evaluates the current impact of AI on radiology, revealing significant usage patterns and clinical implications, thereby guiding future integration strategies to enhance efficiency and patient care in clinical radiology.Key PointsThe survey examines ESR member's views about the impact of AI on radiology practice.AI use is relevant in CT and MRI, with varying impacts on job roles.AI tools enhance clinical efficiency but require radiologist oversight for patient acceptance.
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页数:13
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  • [1] [Anonymous], 2017, European Parliament resolution of 4 April 2017 on Palm oil and deforestation of rainforests
  • [2] Interventional radiology and artificial intelligence in radiology: Is it time to enhance the vision of our medical students?
    Auloge, Pierre
    Garnon, Julien
    Robinson, Joey Marie
    Dbouk, Sarah
    Sibilia, Jean
    Braun, Marc
    Vanpee, Dominique
    Koch, Guillaume
    Cazzato, Roberto Luigi
    Gangi, Afshin
    [J]. INSIGHTS INTO IMAGING, 2020, 11 (01)
  • [3] Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?
    Baselli, Giuseppe
    Codari, Marina
    Sardanelli, Francesco
    [J]. EUROPEAN RADIOLOGY EXPERIMENTAL, 2020, 4 (01)
  • [4] Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications
    Bhayana, Rajesh
    [J]. RADIOLOGY, 2024, 310 (01)
  • [5] Value-based radiology: what is the ESR doing, and what should we do in the future?
    Brady, Adrian P.
    Visser, Jacob
    Frija, Guy
    Bargallo, Nuria
    Rockall, Andrea
    Brkljacic, Boris
    Fuchsjaeger, Michael
    Birch, Judy
    Becker, Minerva
    Kroencke, Thomas
    [J]. INSIGHTS INTO IMAGING, 2021, 12 (01)
  • [6] Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology
    Brkljacic, Boris
    Derchi, Lorenzo E.
    Hamm, Bernd
    Fuchsjager, Michael
    Krestin, Gabriel
    Dewey, Marc
    Parizel, Paul
    Clark, Jonathan
    Codari, Marina
    Melazzini, Luca
    Morozov, Sergey P.
    van Kuijk, Cornelis C.
    Sconfienza, Luca M.
    Sardanelli, Francesco
    [J]. INSIGHTS INTO IMAGING, 2019, 10 (01)
  • [7] AI applications to medical images: From machine learning to deep learning
    Castiglioni, Isabella
    Rundo, Leonardo
    Codari, Marina
    Leo, Giovanni Di
    Salvatore, Christian
    Interlenghi, Matteo
    Gallivanone, Francesca
    Cozzi, Andrea
    D'Amico, Natascha Claudia
    Sardanelli, Francesco
    [J]. PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2021, 83 : 9 - 24
  • [8] Machine Learning for Medical Imaging1
    Erickson, Bradley J.
    Korfiatis, Panagiotis
    Akkus, Zeynettin
    Kline, Timothy L.
    [J]. RADIOGRAPHICS, 2017, 37 (02) : 505 - 515
  • [9] Resident education in radiology in Europe including entrustable professional activities: results of an ESR survey
    European Soc Radiology ESR
    [J]. INSIGHTS INTO IMAGING, 2023, 14 (01)
  • [10] Current practical experience with artificial intelligence in clinical radiology: a survey of the European Society of Radiology
    Becker C.D.
    Kotter E.
    Fournier L.
    Martí-Bonmatí L.
    [J]. INSIGHTS INTO IMAGING, 2022, 13 (01)