{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T05:35:31Z","timestamp":1790919331836,"version":"4.1.0"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"vor","delay-in-days":296,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NHSX"},{"name":"AI Award in Health and Care","award":["AI_AWARD02169"],"award-info":[{"award-number":["AI_AWARD02169"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) algorithms have been developed to identify vertebral fractures through reanalysis of existing CT scans. This study describes a real-world case study of the deployment of an AI solution in the NHS, from information governance (IG) and technology (IT) perspectives, to inform best practice recommendations. Five NHS hospitals were selected to deploy the Nanox AI solution to identify vertebral fractures from existing CT images. The journey to IG and IT assurance was described and used to inform recommendations. The time from contract signing to IG assurance ranged between 5 and 13\u2009months. The period from IG assurance to the analysis of the first patient scan ranged from 7 to 12\u2009months, excluding 1 site withdrawing from the process. Each site required different IG documents: Data Protection Impact Assessment (5\/5 sites), Data Protection Agreement (2\/5 sites), Digital Technology Assessment Criteria (2\/5 sites). IT implementation delays included third-party supplier coordination, NHS IT staff availability, and local capability. Based on the observed challenges, 6 best practice recommendations are proposed to address current challenges to AI adoption in radiology settings in the NHS to support IG and 8 to support IT implementation services. Significant challenges remain if AI is to be routinely used to identify vertebral fractures. The proposed recommendations provide a pathway to improve effective and efficient AI deployment. This study proposes recommendations from IG and IT perspectives to improve the local deployment of AI in the NHS.<\/jats:p>","DOI":"10.1093\/bjrai\/ubaf017","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T13:51:52Z","timestamp":1761313912000},"source":"Crossref","is-referenced-by-count":1,"title":["Lessons learned from a multi-centre implementation of an artificial intelligence algorithm to detect vertebral fractures for radiology, information technology, information governance and clinical leads"],"prefix":"10.1093","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1283-7839","authenticated-orcid":false,"given":"Rohit","family":"Vijjhalwar","sequence":"first","affiliation":[{"name":"Translational and Experimental Sciences Division, University Hospital Southampton NHS Foundation Trust , Southampton, SO16 6YD,","place":["United 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University Hospitals NHS Foundation Trust , Oxford, OX3 7HE,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raz","family":"Edwards","sequence":"additional","affiliation":[{"name":"Data Security and Protection, The Royal Wolverhampton NHS Trust , Wolverhampton, WV1 9TD,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amrita","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Radiology, Frimley Health NHS Foundation Trust , Frimley, GU16 7UJ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Curtis","sequence":"additional","affiliation":[{"name":"MRC Lifecourse Epidemiology Centre, University of Southampton , Southampton, SO16 6YD,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenneth E S","family":"Poole","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Cambridge, NIHR Cambridge Biomedical Research Centre , Cambridge, CB2 0QQ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yotam","family":"Kimmel","sequence":"additional","affiliation":[{"name":"Nanox AI , Petah Tikva, 4970602,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oren","family":"Shalem","sequence":"additional","affiliation":[{"name":"Nanox AI , Petah Tikva, 4970602,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Stone","sequence":"additional","affiliation":[{"name":"Geriatric Medicine and General Internal Medicine, Cardiff and Vale University Health Board, Cardiff, CF11 4XW, \u00a0","place":["United Kingdom"]},{"name":"School of Medicine, Cardiff University, Cardiff, CF14 4YS,","place":["United 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