Quantifying the AI readiness gap: An international, multidisciplinary assessment of artificial intelligence literacy in the radiation oncology community

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dc.contributor.authorMalone, Ciaranen
dc.contributor.authorCallens, Dylanen
dc.contributor.authorForde, Elizabethen
dc.contributor.authorLeech, Michelleen
dc.contributor.authorCardenas, Carlosen
dc.contributor.authorGooding, Mark J.en
dc.contributor.authorRyan, Samanthaen
dc.contributor.authorThirion, Pierreen
dc.contributor.authorFitzpatrick, Claireen
dc.contributor.authorO’Donovan, Theresaen
dc.contributor.authorCarver, Antonyen
dc.contributor.authorGiron, Irene Hernandezen
dc.contributor.authorBrowne, Darragh P.en
dc.contributor.authorRogerson, Catherineen
dc.contributor.authorMcClean, Brendanen
dc.contributor.authorHeijmen, B.J.M.en
dc.contributor.authorHanna, Gerard G.en
dc.contributor.authorNicholson, Jillen
dc.contributor.funderSt. Luke’s Institute of Cancer Research in Dublinen
dc.date.accessioned2026-10-27T11:45:38Z
dc.date.available2026-10-27T11:45:38Z
dc.date.issued2026-06-01en
dc.description.abstractPurpose: The rapid integration of artificial intelligence (AI) into imaging-intensive fields like radiation oncology (RO) is transforming the clinical workforce from manual operators to supervisory validators, yet the baseline competencies required for safe oversight remain undefined. Here we present the first consensus-built, validated international knowledge assessment of AI literacy to quantify the RO knowledge profile. Methods: A multidisciplinary, international panel from clinical practice, academia, and industry developed a 22-item knowledge assessment mapped to core AI-in-radiotherapy concepts. Items underwent a three-round consensus process with blinded initial voting, achieving > 80% panel agreement. The instrument was deployed worldwide via a multilingual web application, and demonstrated excellent internal consistency (Cronbach’s α = 0.90) and strong discrimination (mean: 0.63). Results: 760 individuals engaged with the assessment; 528 completed all items. Significant role-specific disparities emerged: Medical Physicists demonstrated higher scores than Radiation Oncologists and Radiation Therapists (p < 0.001). However, required competencies vary by role. While the workforce exhibited strong intuition regarding data-centric risks such as bias and data drift, widespread deficits were identified in fundamental model mechanics and interpretation of prediction uncertainty. This differential performance hinders the shared vocabulary necessary to describe model failures or distinguish between simpler models and advanced foundational architectures capable of adapting to complex clinical data, both labelled “AI”. Our analysis also challenges traditional educational paradigms: targeted workshops proved as effective as formal degrees, and knowledge score did not correlate with career stage. Conclusions: These results establish a global benchmark for AI readiness. While clinical staff may not require AI development expertise, our findings demonstrate significant value in targeted training to build shared AI vocabulary, ensuring clinical adoption does not outpace safe oversight.en
dc.description.sponsorshipSLICR 2023/7542en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid101210en
dc.identifier.citationMalone, C. et al. (2026) “Quantifying the AI readiness gap: An international, multidisciplinary assessment of artificial intelligence literacy in the radiation oncology community,” Clinical and Translational Radiation Oncology, 59, (10pp), https://doi.org/10.1016/j.ctro.2026.101210en
dc.identifier.doi10.1016/j.ctro.2026.101210en
dc.identifier.endpage10en
dc.identifier.issn2405-6308en
dc.identifier.journaltitleClinical and Translational Radiation Oncologyen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/19501
dc.identifier.volume59en
dc.language.isoenen
dc.publisherElsevier BVen
dc.relation.projectResearch Fellowshipen
dc.rights© 2026, authors. Published by Elsevier B.V. on behalf of European Society for Radiotherapy and Oncology. This is an open access article under the CC BY licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectArtificial intelligenceen
dc.subjectRadiation oncologyen
dc.subjectClinical competenceen
dc.subjectMedical educationen
dc.subjectPsychometricsen
dc.subjectHealth workforceen
dc.subject[Medicine]en
dc.titleQuantifying the AI readiness gap: An international, multidisciplinary assessment of artificial intelligence literacy in the radiation oncology communityen
dc.typeArticle (peer-reviewed)en
dc.typejournal-articleen
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