{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:35:54Z","timestamp":1792794954852,"version":"4.5.0"},"reference-count":73,"publisher":"Oxford University Press (OUP)","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BJR"],"abstract":"<jats:p>Technological advancements in computer science have started to bring artificial intelligence (AI) from the bench closer to the bedside. While there is still lots to do and improve, AI models in medical imaging and radiotherapy are rapidly being developed and increasingly deployed in clinical practice. At the same time, AI governance frameworks are still under development. Clinical practitioners involved with procuring, deploying, and adopting AI tools in the UK should be well-informed about these AI governance frameworks. This scoping review aimed to map out available literature on AI governance in the UK, focusing on medical imaging and radiotherapy. Searches were performed on Google Scholar, Pubmed, and the Cochrane Library, between June and July 2022. Of 4225 initially identified sources, 35 were finally included in this review. A comprehensive conceptual AI governance framework was proposed, guided by the need for rigorous AI validation and evaluation procedures, the accreditation rules and standards, and the fundamental ethical principles of AI. Fairness, transparency, trustworthiness, and explainability should be drivers of all AI models deployed in clinical practice. Appropriate staff education is also mandatory to ensure AI\u2019s safe and responsible use. Multidisciplinary teams under robust leadership will facilitate AI adoption, and it is crucial to involve patients, the public, and practitioners in decision-making. Collaborative research should be encouraged to enhance and promote innovation, while caution should be paid to the ongoing auditing of AI tools to ensure safety and clinical effectiveness.<\/jats:p>","DOI":"10.1259\/bjr.20221157","type":"journal-article","created":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T09:27:16Z","timestamp":1695634036000},"source":"Crossref","is-referenced-by-count":51,"title":["Black box no more: a scoping review of AI governance frameworks to guide procurement and adoption of AI in medical imaging and radiotherapy in the UK"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1378-6631","authenticated-orcid":false,"given":"Nikolaos","family":"Stogiannos","sequence":"first","affiliation":[{"name":"Discipline of Medical Imaging and Radiation Therapy, University College Cork, Cork, Ireland"},{"name":"Division of Midwifery & Radiography, City, University of London, London, United Kingdom"},{"name":"Medical Imaging Department, Corfu 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