{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T22:44:04Z","timestamp":1783032244929,"version":"3.54.6"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":292,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"crossref","award":["NIHR-301332"],"award-info":[{"award-number":["NIHR-301332"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) is transforming radiology, with nearly 80% of approved AI as medical devices (AIaMDs) being imaging-related. As AI adoption accelerates, radiology training programs must evolve to equip future radiologists with the skills to critically evaluate, implement, and integrate AI into clinical practice. However, despite AI's growing role, its inclusion in medical curricula remains inconsistent, and assessment of AI competency is lacking. This review explores the current state of AI in UK medical training curricula with a more in-depth focus on radiology. We discuss the potential impact of AI on competency evaluations, including the Fellowship of the Royal College of Radiologists (FRCR) examinations, Annual Review of Competence Progression (ARCP), and on-call assessments. Additionally, we examine how AI-driven educational resources, such as AI-assisted training platforms, could enhance radiology education. To future-proof radiology training and careers, we propose strategies to evaluate AI literacy including nationalized structured AI teaching, and AI-focused assessments. Addressing these challenges will be crucial in ensuring that radiologists remain at the forefront of digital healthcare transformation while maintaining their core diagnostic expertise.<\/jats:p>","DOI":"10.1093\/bjro\/tzaf026","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T20:17:01Z","timestamp":1760559421000},"source":"Crossref","is-referenced-by-count":2,"title":["Radiology AI training and assessment\u2014challenges, innovations, and a path forward"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8909-6560","authenticated-orcid":false,"given":"Girija","family":"Agarwal","sequence":"first","affiliation":[{"name":"Department of Clinical Radiology, St Mary's Hospital, Imperial College Healthcare Trust , London W2 1NY,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kavish","family":"Maroo","sequence":"additional","affiliation":[{"name":"University College London 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