Generative AI and large language models in radiography education: Possibilities, obstacles, and expectations for academic staff

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dc.contributor.authorRainey, Clare
dc.contributor.authorMcLaughlin, L.
dc.contributor.authorEngland, Andrew
dc.contributor.authorMalamateniou, C.
dc.contributor.authorMcFadden, S. L.
dc.contributor.authorWoznitza, N.
dc.date.accessioned2026-05-14T14:50:21Z
dc.date.available2026-05-14T14:50:21Z
dc.date.embargoedUntil2027-05-01
dc.date.issued2026-05-01
dc.description.abstractArtificial Intelligence (AI) has become a part of day-to-day life for many. This includes the use of AI in healthcare, where its use has been proposed to improve accuracy, make efficiencies in workflows and streamline administrative processes. The recency of the widespread use of advanced technologies has resulted in knowledge gaps for some. There remains disparity in the opinion of the public about AI in general and AI used in healthcare [1]. In the case of modern forms of AI, willingness and acceptance have been proposed to be, in part, determined by generational preferences [2], [3], [4]. The majority of the undergraduate student population in the UK is under 21 years of age (74.6%, n = 1126,070 in the academic year 2023–24) [5]. This demographic is technologically adept, as they have grown up with advanced technology and are willing to seek and use emergent technologies to their advantage in many tasks, including learning. However, a recent survey of 5218 so-called ‘Gen Z’ respondents indicated that while they are comfortable with AI use in their daily lives, they may be ‘overconfident’ in their abilities to use it critically.en
dc.description.statusPeer reviewed
dc.description.versionAccepted Version
dc.description.versionPublished Version
dc.format.extent4
dc.format.extent152202
dc.format.mimetypeapplication/pdf
dc.identifier.articleid102435
dc.identifier.authororcidRainey, Clare§0000-0003-0449-8646
dc.identifier.authororcidMcLaughlin, L.§0000-0001-7825-6821
dc.identifier.authororcidEngland, Andrew§0000-0001-6333-7776
dc.identifier.authororcidMalamateniou, C.
dc.identifier.authororcidMcFadden, S. L.
dc.identifier.authororcidWoznitza, N.
dc.identifier.citationRainey, C., McLaughlin, L., England, A., Malamateniou, C., McFadden, S. L. and Woznitza, N. (2026) 'Generative AI and large language models in radiography education: Possibilities, obstacles, and expectations for academic staff', Journal of Medical Imaging and Radiation Sciences, 57(4), 102435 (4pp). https://doi.org/10.1016/j.jmir.2026.102435
dc.identifier.doi10.1016/j.jmir.2026.102435
dc.identifier.endpage4
dc.identifier.issn1939-8654
dc.identifier.issued4
dc.identifier.journaltitleJournal of Medical Imaging and Radiation Sciences
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/18766
dc.identifier.urlhttps://www.scopus.com/pages/publications/105037472906
dc.identifier.volume57
dc.language.isoen
dc.publisherElsevier
dc.rights© 2026. Published by Elsevier Inc. on behalf of Canadian Association of Medical Radiation Technologists.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectArtifical intelligence
dc.subjectGenerative AI
dc.subjectHigher education
dc.subjectLarge language models
dc.subjectRadiography education
dc.subjectStudents
dc.subject[Medicine]
dc.subjectResearch and Theory
dc.subjectRadiological and Ultrasound Technology
dc.subjectHealth Professions (miscellaneous)
dc.subjectRadiology, Nuclear Medicine and Imaging
dc.subjectAssessment and Diagnosis
dc.titleGenerative AI and large language models in radiography education: Possibilities, obstacles, and expectations for academic staffen
dc.typeArticle (non peer-reviewed)
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