Radiographer education and learning in artificial intelligence (REAL-AI): a survey of radiographers, radiologists, and students’ knowledge of and attitude to education on AI

Simple item page
dc.contributor.authorDoherty, Geraldineen
dc.contributor.authorMcLaughlin, Lauraen
dc.contributor.authorHughes, Ciaraen
dc.contributor.authorMcConnell, Jonathanen
dc.contributor.authorBond, Raymonden
dc.contributor.authorMcFadden, Sonyia L.en
dc.contributor.funderEuropean Federation of Radiological Societiesen
dc.contributor.funderCollege of Radiographersen
dc.date.accessioned2025-01-27T10:04:59Z
dc.date.available2025-01-27T10:04:59Z
dc.date.issued2024en
dc.description.abstractIntroduction: In Autumn 2023, amendments to the Health and Care Professions Councils (HCPC) Standards of Proficiency for Radiographers were introduced requiring clinicians to demonstrate awareness of the principles of AI and deep learning technology, and its application to practice’ (HCPC 2023; standard 12.25). With the rapid deployment of AI in departments, staff must be prepared to implement and utilise AI. AI readiness is crucial for adoption, with education as a key factor in overcoming fear and resistance. This survey aimed to assess the current understanding of AI among students and qualified staff in clinical practice. Methods: A survey targeting radiographers (diagnostic and therapeutic), radiologists and students was conducted to gather demographic data and assess awareness of AI in clinical practice. Hosted online via JISC, the survey included both closed and open-ended questions and was launched in March 2023 at the European Congress of Radiology (ECR). Results: A total of 136 responses were collected from participants across 25 countries and 5 continents. The majority were diagnostic radiographers 56.6 %, followed by students 27.2 %, dual-qualified 3.7 % and radiologists 2.9 %. Of the respondents, 30.1 % of respondents indicated that their highest level of qualification was a Bachelor's degree, 29.4 % stated that they are currently using AI in their role, whilst 27 % were unsure. Only 10.3 % had received formal AI training. Conclusion: This study reveals significant gaps in training and understanding of AI among medical imaging staff. These findings will guide further research into AI education for medical imaging professionals. Implications for practice: This paper lays foundations for future qualitative studies on the provision of AI education for medical imaging professionals, helping to prepare the workforce for the evolving role of AI in medical imaging.en
dc.description.sponsorshipCollege of Radiographers (Industry Partnerships Scheme [grant number 229 (AI)0.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDoherty, G., McLaughlin, L., Hughes, C., McConnell, J., Bond, R. and McFadden, S. (2024) ‘Radiographer Education and Learning in Artificial Intelligence (Real-ai): A survey of radiographers, radiologists, and students’ knowledge of and attitude to education on AI’, Radiography, 30, pp. 79–87. https://doi.org/10.1016/j.radi.2024.10.010en
dc.identifier.doihttps://doi.org/10.1016/j.radi.2024.10.010en
dc.identifier.eissn1532-2831en
dc.identifier.endpage87en
dc.identifier.issn1078-8174en
dc.identifier.journaltitleRadiographyen
dc.identifier.startpage79en
dc.identifier.urihttps://hdl.handle.net/10468/16896
dc.identifier.volume30en
dc.language.isoenen
dc.publisherElsevieren
dc.rights© 2024, the Authors. Published by Elsevier Ltd on behalf of The College of Radiographers. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectArtificial intelligence (AI)en
dc.subjectEducationen
dc.subjectMedical imagingen
dc.subjectTechnology acceptanceen
dc.subjectCPDen
dc.titleRadiographer education and learning in artificial intelligence (REAL-AI): a survey of radiographers, radiologists, and students’ knowledge of and attitude to education on AIen
dc.typeArticle (peer-reviewed)en
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
1-s2.0-S1078817424003080-main.pdf
Size:
544.73 KB
Format:
Adobe Portable Document Format
Description:
Published Version
Download
Thumbnail Image
Name:
ScienceDirect_files_27Jan2025_10-05-18.170.zip
Size:
187.52 KB
Format:
http://www.iana.org/assignments/media-types/application/zip
Description:
Supplementary data
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Medicine - Journal Articles