{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T23:21:31Z","timestamp":1791847291745,"version":"4.3.4"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","award":["NIHR-CDF-2017-10-037"],"award-info":[{"award-number":["NIHR-CDF-2017-10-037"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","award":["NIHR-301322"],"award-info":[{"award-number":["NIHR-301322"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"DOI":"10.1007\/s00330-025-11975-6","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T14:14:00Z","timestamp":1759932840000},"page":"2083-2090","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Safe for kids? AI medical devices in radiology overlook paediatric suitability"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3449-8017","authenticated-orcid":false,"given":"Brendan S.","family":"Kelly","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward","family":"Antram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Owen","family":"Arthurs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan C.","family":"Shelmerdine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,8]]},"reference":[{"key":"11975_CR1","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1177\/08465371231222229","volume":"75","author":"AP Brady","year":"2024","unstructured":"Brady AP, Allen B, Chong J et al (2024) Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. a multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA. Can Assoc Radiol J 75:226\u2013244. https:\/\/doi.org\/10.1177\/08465371231222229","journal-title":"Can Assoc Radiol J"},{"key":"11975_CR2","doi-asserted-by":"publisher","unstructured":"Onitiu D, Wachter S, Mittelstadt B (2024) How AI challenges the medical device regulation: patient safety, benefits, and intended uses. J Law Biosc. https:\/\/doi.org\/10.1093\/jlb\/lsae007","DOI":"10.1093\/jlb\/lsae007"},{"key":"11975_CR3","doi-asserted-by":"publisher","first-page":"2294","DOI":"10.1007\/s00330-023-10288-w","volume":"34","author":"SC Shelmerdine","year":"2024","unstructured":"Shelmerdine SC (2024) Revolutionising paediatric radiology: the future impact of artificial intelligence. Eur Radiol 34:2294\u20132296. https:\/\/doi.org\/10.1007\/s00330-023-10288-w","journal-title":"Eur Radiol"},{"key":"11975_CR4","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1016\/j.jacr.2023.06.004","volume":"20","author":"BJ Nelson","year":"2023","unstructured":"Nelson BJ, Zeng R, Sammer MBK et al (2023) An FDA guide on indications for use and device reporting of artificial intelligence-enabled devices: significance for pediatric use. J Am Coll Radiol 20:738\u2013741. https:\/\/doi.org\/10.1016\/j.jacr.2023.06.004","journal-title":"J Am Coll Radiol"},{"key":"11975_CR5","doi-asserted-by":"crossref","unstructured":"Kelly BS, Clifford S, Judge C et al (2024) A survey of paediatric radiology artificial intelligence. Preprint at https:\/\/www.medrxiv.org\/content\/10.1101\/2024.09.19.24313885v1.full","DOI":"10.1101\/2024.09.19.24313885"},{"key":"11975_CR6","doi-asserted-by":"publisher","first-page":"713681","DOI":"10.3389\/fradi.2021.713681","volume":"1","author":"D Martin","year":"2021","unstructured":"Martin D, Tong E, Kelly B et al (2021) Current perspectives of artificial intelligence in pediatric neuroradiology: an overview. Front Radiol 1:713681. https:\/\/doi.org\/10.3389\/fradi.2021.713681","journal-title":"Front Radiol"},{"key":"11975_CR7","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1016\/j.jacr.2023.06.003","volume":"20","author":"MBK Sammer","year":"2023","unstructured":"Sammer MBK, Akbari YS, Barth RA et al (2023) Use of artificial intelligence in radiology: impact on pediatric patients, a white paper from the ACR pediatric AI workgroup. J Am Coll Radiol 20:730\u2013737. https:\/\/doi.org\/10.1016\/j.jacr.2023.06.003","journal-title":"J Am Coll Radiol"},{"key":"11975_CR8","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1007\/s00247-021-05086-9","volume":"52","author":"JP Otjen","year":"2022","unstructured":"Otjen JP, Moore MM, Romberg EK et al (2022) The current and future roles of artificial intelligence in pediatric radiology. Pediatr Radiol 52:2065\u20132073. https:\/\/doi.org\/10.1007\/s00247-021-05086-9","journal-title":"Pediatr Radiol"},{"key":"11975_CR9","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s13244-024-01616-9","volume":"15","author":"KG Leeuwen","year":"2024","unstructured":"Leeuwen KG, van, Hedderich DM, Harvey H, Schalekamp S (2024) How AI should be used in radiology: assessing ambiguity and completeness of intended use statements of commercial AI products. Insights Imaging 15:51. https:\/\/doi.org\/10.1186\/s13244-024-01616-9","journal-title":"Insights Imaging"},{"key":"11975_CR10","doi-asserted-by":"publisher","first-page":"7058","DOI":"10.1007\/s00330-021-07781-5","volume":"31","author":"M Huisman","year":"2021","unstructured":"Huisman M, Ranschaert E, Parker W et al (2021) An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. Eur Radiol 31:7058\u20137066. https:\/\/doi.org\/10.1007\/s00330-021-07781-5","journal-title":"Eur Radiol"},{"key":"11975_CR11","doi-asserted-by":"publisher","unstructured":"Mann SP, Cohen IG, Minssen T (2024) The EU AI act: implications for U.S. health care. NEJM AI. https:\/\/doi.org\/10.1056\/aip2400449","DOI":"10.1056\/aip2400449"},{"key":"11975_CR12","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1186\/s41747-024-00485-7","volume":"8","author":"F Pesapane","year":"2024","unstructured":"Pesapane F, Cuocolo R, Sardanelli F (2024) The Picasso\u2019s skepticism on computer science and the dawn of generative AI: questions after the answers to keep \u201cmachines-in-the-loop. Eur Radiol Exp 8:81. https:\/\/doi.org\/10.1186\/s41747-024-00485-7","journal-title":"Eur Radiol Exp"},{"key":"11975_CR13","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1186\/s13244-024-01801-w","volume":"15","author":"M Zanardo","year":"2024","unstructured":"Zanardo M, Visser JJ, Colarieti A et al (2024) Impact of AI on radiology: a EuroAIM\/EuSoMII 2024 survey among members of the European Society of Radiology. Insights Imaging 15:240. https:\/\/doi.org\/10.1186\/s13244-024-01801-w","journal-title":"Insights Imaging"},{"key":"11975_CR14","doi-asserted-by":"publisher","first-page":"3797","DOI":"10.1007\/s00330-021-07892-z","volume":"31","author":"Leeuwen","year":"2021","unstructured":"Leeuwen, van KG, Schalekamp S, Rutten MJCM et al (2021) Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol 31:3797\u20133804. https:\/\/doi.org\/10.1007\/s00330-021-07892-z","journal-title":"Eur Radiol"},{"key":"11975_CR15","unstructured":"HaRi (2024) Hardian regulatory intelligence. Available via http:\/\/hari.hardianhealth.com\/. Accessed 23 Nov 2024"},{"key":"11975_CR16","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1001\/jamapediatrics.2024.5437","volume":"179","author":"RCL Brewster","year":"2025","unstructured":"Brewster RCL, Nagy M, Wunnava S, Bourgeois FT (2025) US FDA approval of pediatric artificial intelligence and machine learning-enabled medical devices. JAMA Pediatr 179:212\u2013214. https:\/\/doi.org\/10.1001\/jamapediatrics.2024.5437","journal-title":"JAMA Pediatr"},{"key":"11975_CR17","doi-asserted-by":"publisher","unstructured":"Shelmerdine SC (2024) Rethinking our relationship with AI: For better or worse, richer or poorer? Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-024-11007-9","DOI":"10.1007\/s00330-024-11007-9"},{"key":"11975_CR18","doi-asserted-by":"publisher","unstructured":"Lee L, Salami RK, Martin H et al (2024) How I would like AI used for my imaging\u201d: children and young persons\u2019 perspectives. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-024-10839-9","DOI":"10.1007\/s00330-024-10839-9"},{"key":"11975_CR19","doi-asserted-by":"publisher","first-page":"e195","DOI":"10.1016\/s2589-7500(20)30292-2","volume":"3","author":"UJ Muehlematter","year":"2021","unstructured":"Muehlematter UJ, Daniore P, Vokinger KN (2021) Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015\u201320): a comparative analysis. Lancet Digital Heal 3:e195\u2013e203. https:\/\/doi.org\/10.1016\/s2589-7500(20)30292-2","journal-title":"Lancet Digital Heal"},{"key":"11975_CR20","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1038\/s41572-022-00337-x","volume":"8","author":"PB Sporns","year":"2022","unstructured":"Sporns PB, Fullerton HJ, Lee S et al (2022) Childhood stroke. Nat Rev Dis Prim 8:12. https:\/\/doi.org\/10.1038\/s41572-022-00337-x","journal-title":"Nat Rev Dis Prim"},{"key":"11975_CR21","doi-asserted-by":"publisher","unstructured":"Colleran GC, Fossmark M, Rosendahl K et al (2024) ESR Essentials: imaging of suspected child abuse\u2014practice recommendations by the European Society of Paediatric Radiology. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-024-11052-4","DOI":"10.1007\/s00330-024-11052-4"},{"key":"11975_CR22","doi-asserted-by":"publisher","first-page":"357","DOI":"10.3171\/2020.7.peds20447","volume":"27","author":"G Boulouis","year":"2021","unstructured":"Boulouis G, Stricker S, Benichi S et al (2021) Etiology of intracerebral hemorrhage in children: cohort study, systematic review, and meta-analysis. J Neurosurg: Pediatr 27:357\u2013363. https:\/\/doi.org\/10.3171\/2020.7.peds20447","journal-title":"J Neurosurg: Pediatr"},{"key":"11975_CR23","doi-asserted-by":"publisher","first-page":"5873","DOI":"10.1007\/s00330-024-10664-0","volume":"34","author":"M Huisman","year":"2024","unstructured":"Huisman M, Ginneken B, van, Harvey H (2024) The emperor has few clothes: a realistic appraisal of current AI in radiology. Eur Radiol 34:5873\u20135875. https:\/\/doi.org\/10.1007\/s00330-024-10664-0","journal-title":"Eur Radiol"},{"key":"11975_CR24","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1016\/j.jacr.2023.05.020","volume":"20","author":"MBK Sammer","year":"2023","unstructured":"Sammer MBK, Frush DP (2023) Image Gently\u00ae \u201cincludes\u201d image intelliGentlyTM. J Am Coll Radiol 20:1290\u20131291. https:\/\/doi.org\/10.1016\/j.jacr.2023.05.020","journal-title":"J Am Coll Radiol"},{"key":"11975_CR25","doi-asserted-by":"publisher","unstructured":"Mitchell M, Wu S, Zaldivar A et al (2019) Model cards for model reporting. In: Proceedings of the conference on fairness, accountability, and transparency. ACM Digital Library, pp 220\u2013229. https:\/\/doi.org\/10.1145\/3287560.3287596","DOI":"10.1145\/3287560.3287596"},{"key":"11975_CR26","unstructured":"OECD (2025) Model cards\u2014OECD.AI. Available via https:\/\/oecd.ai\/en\/catalogue\/tools\/model-cards. Accessed 21 Jan 2025"},{"key":"11975_CR27","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1038\/s41746-023-00898-5","volume":"6","author":"V Muralidharan","year":"2023","unstructured":"Muralidharan V, Burgart A, Daneshjou R, Rose S (2023) Recommendations for the use of pediatric data in artificial intelligence and machine learning ACCEPT-AI. npj Digit Med 6:166. https:\/\/doi.org\/10.1038\/s41746-023-00898-5","journal-title":"npj Digit Med"},{"key":"11975_CR28","unstructured":"FDA (2025) Artificial intelligence-enabled device software functions: lifecycle management and marketing submission recommendations. Available via https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/artificial-intelligence-enabled-device-software-functions-lifecycle-management-and-marketing. Accessed 28 Jan 2025"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11975-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-025-11975-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11975-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T13:20:05Z","timestamp":1772716805000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-025-11975-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,8]]},"references-count":28,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["11975"],"URL":"https:\/\/doi.org\/10.1007\/s00330-025-11975-6","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,8]]},"assertion":[{"value":"16 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The scientific guarantor of this publication is B.S.K.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"B.S.K. is an NDTP Dr Richard Steevens and ESOR Fellow in AI and Paediatric Radiology. He is also supported by the Dean Medal for research by MS Ireland. He is the Social Media Editor for\n                      European Radiology\n                      and, as such, did not participate in the selection or review processes for this article. S.C.S. is supported by an NIHR Advanced Fellowship Award (NIHR-301322). S.C.S. declares non-financial research collaborations with Gleamer and Milvue. S.C.S. has previously received honoraria for keynote speaking from Bayer Plc. The remaining authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was not required for this study because it was a review of publicly available data.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was not required because it was a review of publicly available data.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"NA.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}