{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T01:17:04Z","timestamp":1781313424657,"version":"3.54.1"},"reference-count":81,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T00:00:00Z","timestamp":1717632000000},"content-version":"vor","delay-in-days":157,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NHS AI Health and Social Care"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Clinical care, workflow, and patient outcomes within National Health Service (NHS) radiology services will potentially benefit significantly in coming years with the integration of artificial intelligence (AI) into various specialty areas. This article summarizes an expert roundtable discussion, convened by the British Institute of Radiology, comprised experts from clinical, NHS management and industry backgrounds. Facilitators and barriers to both implementation and integration of AI platforms into daily practice are discussed alongside examples including AI in emergency care and breast screening. Issues addressed include the need to generate more real-world evidence that helps developers and service providers to determine the optimal way for patients to reap the benefits of AI with faster reporting turnaround time, faster diagnoses, and treatment times, for example. To ensure the effective and timely wide-scale implementation, adoption requires a national policy drive and national level coordination but specific, local needs of NHS Trusts and any particular service within Trusts will also need to be met. By embedding AI-capabilities into scanners, ultrasound-devices, X-ray equipment, and Picture archiving and communication system (PACS), local services will benefit, while AI applications can streamline workflows across and between departments and potentially Trusts. AI predictive analytics and systems engineering can be used to better manage patient volume and movement. In the short term, education, training, and confidence in adapting and adopting AI in radiology require dedicated local, national, and governmental effort but the future benefits promise to justify the means.<\/jats:p>","DOI":"10.1093\/bjrai\/ubae009","type":"journal-article","created":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T17:14:39Z","timestamp":1717694079000},"source":"Crossref","is-referenced-by-count":6,"title":["Adoption, orchestration, and deployment of artificial intelligence within the National Health Service\u2014facilitators and barriers: an expert roundtable discussion"],"prefix":"10.1093","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7867-5468","authenticated-orcid":false,"given":"Gerald","family":"Lip","sequence":"first","affiliation":[{"name":"University of Aberdeen , Aberdeen, AB25 2ZD, United Kingdom"},{"name":"North East Scotland Breast Screening Programme, NHS Grampian , Aberdeen, AB25 2XF, United 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