Awareness, perceptions, and educational preferences for AI among radiography students: A cross sectional survey

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dc.contributor.authorOglat, Ammar A.en
dc.contributor.authorAbukhalil, Talaen
dc.contributor.authorIsawi, Israa H.en
dc.contributor.authorAlzyoud, Kholouden
dc.contributor.authorOqlat, Ahmed A.en
dc.contributor.authorSalman, Mohammed Dawooden
dc.contributor.authorAlnawafleh, Tasnim M.en
dc.contributor.authorBani-Ahmad, Mo’menen
dc.date.accessioned2026-10-22T15:27:06Z
dc.date.available2026-10-22T15:27:06Z
dc.date.issued2026-06-06en
dc.description.abstractBackground: Artificial intelligence (AI) is gaining increasing significance in radiography. However, undergraduate radiography students often have limited exposure to AI principles and an incomplete understanding of its capabilities and limitations. This study evaluated radiography students’ self-reported familiarity with AI, perceptions of its role in medical imaging, and educational preferences. Methods: A descriptive cross-sectional survey was conducted at a single institution in Jordan between March and August 2024. A self-administered, in-person paper questionnaire, adapted from a previously published survey, was distributed to students attending scheduled teaching sessions. Responses were analysed descriptively using frequencies and percentages. Results: Of the 217 students approached, 180 participated (response rate 82.9%). Only 46.1% of respondents reported good familiarity with AI. Most students perceived AI as having useful applications in medical imaging and supported its integration into university training. However, when AI output conflicted with human judgement, 60.0% of students indicated that they would seek expert opinion, reflecting a cautious approach to AI-supported decision-making. Conclusion: Radiography students reported positive attitudes toward AI and strong support for curricular integration, alongside caution about routine reliance on AI for decision making. Structured educational activities that provide practical exposure, clarify appropriate use cases, and emphasise human oversight may help students use AI safely and effectively in future practice. Plain Language Summary: Artificial intelligence is being used more often in medical imaging, but students may not fully understand it. This study surveyed radiography students in Jordan to understand their knowledge of artificial intelligence, how they view its role, and how they want to learn about it. This study found that students welcomed its use but had limited understanding and preferred to seek expert advice when unsure. This matters because better teaching can help students use these tools safely and confidently in practice.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid102452en
dc.identifier.citationOglat, A.A. et al. (2026) “Awareness, perceptions, and educational preferences for AI among radiography students: A cross sectional survey,” Journal of Medical Imaging and Radiation Sciences, 57(4), (7pp), https://doi.org/10.1016/j.jmir.2026.102452.en
dc.identifier.doi10.1016/j.jmir.2026.102452en
dc.identifier.endpage7en
dc.identifier.issn1939-8654en
dc.identifier.issued4en
dc.identifier.journaltitleJournal of Medical Imaging and Radiation Sciencesen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/19497
dc.identifier.volume57en
dc.language.isoenen
dc.publisherElsevier BVen
dc.rights© 2026, authors. Published by Elsevier Inc. on behalf of Canadian Association of Medical Radiation Technologists. This is an open access article under the CC BY license.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectArtificial intelligenceen
dc.subjectRadiography studentsen
dc.subjectRadiography educationen
dc.subjectPerceptionsen
dc.subjectCurriculumen
dc.subject[CoMHOffice]en
dc.subject[Medicine]en
dc.titleAwareness, perceptions, and educational preferences for AI among radiography students: A cross sectional surveyen
dc.typeArticle (peer-reviewed)en
dc.typejournal-articleen
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