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

Artificial intelligence Diagnostic imaging Radiology Surveys and questionnaires

Journal

Insights into imaging
ISSN: 1869-4101
Titre abrégé: Insights Imaging
Pays: Germany
ID NLM: 101532453

Informations de publication

Date de publication:
07 Oct 2024
Historique:
received: 27 06 2024
accepted: 09 08 2024
medline: 7 10 2024
pubmed: 7 10 2024
entrez: 7 10 2024
Statut: epublish

Résumé

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 STATEMENT: This 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 POINTS: The 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.

Identifiants

pubmed: 39373853
doi: 10.1186/s13244-024-01801-w
pii: 10.1186/s13244-024-01801-w
doi:

Types de publication

Journal Article

Langues

eng

Pagination

240

Informations de copyright

© 2024. The Author(s).

Références

Codari M, Melazzini L, Morozov SP, van Kuijk CC, Sconfienza LM, Sardanelli F (2019) Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights Imaging 10:105. https://doi.org/10.1186/s13244-019-0798-3
doi: 10.1186/s13244-019-0798-3
Castiglioni I, Rundo L, Codari M et al (2021) AI applications to medical images: from machine learning to deep learning. Phys Med 83:9–24. https://doi.org/10.1016/j.ejmp.2021.02.006
doi: 10.1016/j.ejmp.2021.02.006 pubmed: 33662856
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
doi: 10.1186/s41747-018-0061-6 pubmed: 30353365
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
Shi F, Wang J, Shi J et al (2021) Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev Biomed Eng 14:4–15. https://doi.org/10.1109/RBME.2020.2987975
doi: 10.1109/RBME.2020.2987975 pubmed: 32305937
Becker CD, Kotter E, Fournier L, Martí-Bonmatí L (2022) Current practical experience with artificial intelligence in clinical radiology: a survey of the European Society of Radiology. Insights Imaging 13:107. https://doi.org/10.1186/s13244-022-01247-y
doi: 10.1186/s13244-022-01247-y
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577. https://doi.org/10.1148/radiol.2015151169
doi: 10.1148/radiol.2015151169 pubmed: 26579733
Erickson BJ, Korfiatis P, Akkus Z, Kline TL (2017) Machine learning for medical imaging. Radiographics 37:505–515. https://doi.org/10.1148/rg.2017160130
doi: 10.1148/rg.2017160130 pubmed: 28212054
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
doi: 10.2214/AJR.16.17224 pubmed: 28125274
Geis JR, Brady A, Wu CC et al (2019) Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Insights Imaging 10:101. https://doi.org/10.1186/s13244-019-0785-8
doi: 10.1186/s13244-019-0785-8 pubmed: 31571015
Bhayana R (2024) Chatbots and large language models in radiology: a practical primer for clinical and research applications. Radiology. https://doi.org/10.1148/radiol.232756
Pesapane F, Volonté C, Codari M, Sardanelli F (2018) Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imaging 9:745–753. https://doi.org/10.1007/s13244-018-0645-y
doi: 10.1007/s13244-018-0645-y pubmed: 30112675
Scott I, Carter S, Coiera E (2021) Clinician checklist for assessing suitability of machine learning applications in healthcare. BMJ Heal Care Inform 28:e100251. https://doi.org/10.1136/bmjhci-2020-100251
doi: 10.1136/bmjhci-2020-100251
Zrubka Z, Gulacsi L, Pentek M (2022) Time to start using checklists for reporting artificial intelligence in health care and biomedical research: a rapid review of available tools. In: 2022 IEEE 26th international conference on intelligent engineering systems (INES). IEEE, pp 000015–000020
Mongan J, Moy L, Kahn CE (2020) Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2:e200029. https://doi.org/10.1148/ryai.2020200029
doi: 10.1148/ryai.2020200029 pubmed: 33937821
Kocak B, Akinci D’Antonoli T, Mercaldo N et al (2024) METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging 15:8. https://doi.org/10.1186/s13244-023-01572-w
doi: 10.1186/s13244-023-01572-w pubmed: 38228979
Tejani AS, Klontzas ME, Gatti AA et al (2024) Checklist for artificial intelligence in medical imaging (CLAIM): 2024 update. Radiol Artif Intell. https://doi.org/10.1148/ryai.240300
Hamm B, Marti-Bonmati L, Sardanelli F (2024) ESR Journals editors’ joint statement on guidelines for the use of large language models by authors, reviewers, and editors. Eur Radiol. https://doi.org/10.1007/s00330-023-10511-8
Sardanelli F, Castiglioni I, Colarieti A, Schiaffino S, Di Leo G (2023) Artificial intelligence (AI) in biomedical research: discussion on authors’ declaration of AI in their articles title. Eur Radiol Exp 7:2. https://doi.org/10.1186/s41747-022-00316-7
doi: 10.1186/s41747-022-00316-7 pubmed: 36645623
Pinto dos Santos D, Baeßler B (2018) Big data, artificial intelligence, and structured reporting. Eur Radiol Exp 2:42. https://doi.org/10.1186/s41747-018-0071-4
doi: 10.1186/s41747-018-0071-4 pubmed: 30515717
Glielmo P, Fusco S, Gitto S et al (2024) Artificial intelligence in interventional radiology: state of the art. Eur Radiol Exp 8:62. https://doi.org/10.1186/s41747-024-00452-2
doi: 10.1186/s41747-024-00452-2 pubmed: 38693468
Kondylakis H, Ciarrocchi E, Cerda-Alberich L et al (2022) Position of the AI for health imaging (AI4HI) network on metadata models for imaging biobanks. Eur Radiol Exp 6:29. https://doi.org/10.1186/s41747-022-00281-1
doi: 10.1186/s41747-022-00281-1 pubmed: 35773546
Baselli G, Codari M, Sardanelli F (2020) Opening the black box of machine learning in radiology: Can the proximity of annotated cases be a way? Eur Radiol Exp 4:30. https://doi.org/10.1186/s41747-020-00159-0
doi: 10.1186/s41747-020-00159-0 pubmed: 32372200
Auloge P, Garnon J, Robinson JM et al (2020) Interventional radiology and artificial intelligence in radiology: Is it time to enhance the vision of our medical students? Insights Imaging 11:127. https://doi.org/10.1186/s13244-020-00942-y
doi: 10.1186/s13244-020-00942-y pubmed: 33252702
Sardanelli F, Colarieti A (2022) Open issues for education in radiological research: data integrity, study reproducibility, peer-review, levels of evidence, and cross-fertilization with data scientists. Radiol Med 128:133–135. https://doi.org/10.1007/s11547-022-01582-6
doi: 10.1007/s11547-022-01582-6 pubmed: 36586083
Hirvonen J, Becker M, Aronen HJ (2023) Resident education in radiology in Europe including entrustable professional activities: results of an ESR survey. Insights Imaging 14:139. https://doi.org/10.1186/s13244-023-01489-4
doi: 10.1186/s13244-023-01489-4
Brady AP, Visser J, Frija G et al (2021) Value-based radiology: What is the ESR doing, and what should we do in the future? Insights Imaging 12:108. https://doi.org/10.1186/s13244-021-01056-9
doi: 10.1186/s13244-021-01056-9
Mahoney MC, McGinty G, Sanchez GMF et al (2022) Summary of the proceedings of the International Forum 2021: “A more visible radiologist can never be replaced by AI.” Insights Imaging 13:43. https://doi.org/10.1186/s13244-022-01182-y
doi: 10.1186/s13244-022-01182-y
The AI Act: What will the impact be on the medical device industry? https://portolano.it/en/blog/lifesciences/ai-act-impact-medical-device-industry
EU (2017) The European Parliament and the Council of The European Union Regulation (EU) 2017/746 of the European Parliament and of the Council on in vitro diagnostic medical devices and repealing Directive 98/79/EC and Commission Decision 2010/227/EU. https://artificialintelligenceact.eu/
Biden Jr (2023) Executive order on the safe, secure, and trustworthy development and use of artificial intelligence. https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/
Pesapane F, Cuocolo R, Sardanelli F (2024) The Picasso’s skepticism on computer science and the dawn of generative AI: questions after the answers to keep “machines-in-the-loop.” Eur Radiol Exp 8:81. https://doi.org/10.1186/s41747-024-00485-7
doi: 10.1186/s41747-024-00485-7 pubmed: 39046535

Auteurs

Moreno Zanardo (M)

Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy.

Jacob J Visser (JJ)

Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.

Anna Colarieti (A)

Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy.

Renato Cuocolo (R)

Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy.

Michail E Klontzas (ME)

Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece.
Division of Radiology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden.
Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (ICS-FORTH), Crete, Greece.

Daniel Pinto Dos Santos (D)

Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany.
Department of Radiology, University Hospital of Cologne, Cologne, Germany.

Francesco Sardanelli (F)

Lega Italiana per la Lotta contro i Tumori (LILT) Milano Monza Brianza, Milan, Italy. francesco.sardanelli@legatumori.mi.it.

Classifications MeSH