{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T10:11:22Z","timestamp":1788862282381,"version":"build-2803163510"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"University of the Witwatersrand"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pediatr Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Paediatric chest imaging is central to diagnosing respiratory and cardiopulmonary disease, particularly in low- and middle-income countries (LMICs) where pneumonia remains a leading cause of childhood mortality and radiology expertise is scarce. Artificial intelligence (AI) could expand access, standardise quality and support task-shifting in these \u201cdiagnostic deserts,\u201d yet most systems are trained and validated on adult datasets from high-income settings, and paediatric radiographs form only a small minority of major public training cohorts \u2014 raising concerns about safety, generalisability and equity when such models are deployed in children.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To synthesise current applications of AI across paediatric chest radiography, lung ultrasound, computed tomography and MRI, with emphasis on LMIC workflows, and to define what is required for safe, paediatric-specific deployment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>Narrative\/pictorial review of the published literature, complemented by the authors\u2019 real-world evaluation of a generalist vision\u2013language model (MedGemma) on adult and paediatric chest radiographs, illustrated with representative clinical cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Beyond diagnosis, AI shows potential in quality assurance, lung-ultrasound guidance and multilingual reporting. Real-world experience from CAD4Kids and from MedGemma\u2019s evaluation \u2014 including critical failures in detecting pericardial effusion and tuberculosis without explicit clinical context \u2014 illustrates common failure modes and the ethical implications of domain shift. Key challenges include infrastructure constraints, dataset scarcity and the need for age-aware, explainable models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Rather than adapting adult systems, future tools must be designed from inception for children and the environments in which they live, prioritising federated learning, multimodal integration, robust validation across age strata and multilingual communication with caregivers.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00247-026-06671-6","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T13:00:37Z","timestamp":1780491637000},"page":"1933-1944","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions"],"prefix":"10.1007","volume":"56","author":[{"given":"John Joseph","family":"Muringathuparambil","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shamiek","family":"Maharaj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bradley Max","family":"Segal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasreen","family":"Mahomed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"key":"6671_CR1","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/S0140-6736(14)61698-6","volume":"385","author":"L Liu","year":"2015","unstructured":"Liu L, Oza S, Hogan D et al (2015) Global, regional, and national causes of child mortality in 2000\u201313, with projections to inform post-2015 priorities: an updated systematic analysis. Lancet 385:430\u2013440","journal-title":"Lancet"},{"key":"6671_CR2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-14519-w","author":"HJ Shin","year":"2022","unstructured":"Shin HJ, Son NH, Kim MJ, Kim EK (2022) Diagnostic performance of artificial intelligence approved for adults for the interpretation of pediatric chest radiographs. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-022-14519-w","journal-title":"Sci Rep"},{"key":"6671_CR3","doi-asserted-by":"publisher","first-page":"1568","DOI":"10.1007\/s00247-022-05368-w","volume":"52","author":"S Padash","year":"2022","unstructured":"Padash S, Mohebbian MR, Adams SJ et al (2022) Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review. Pediatr Radiol 52:1568\u20131580","journal-title":"Pediatr Radiol"},{"key":"6671_CR4","doi-asserted-by":"crossref","unstructured":"Pal A, Lee JO, Zhang X, Sankarasubbu M, Roh S, Kim WJ, et al (2026) ReXVQA: a large-scale visual question answering benchmark for generalist chest X-ray understanding. Pac Symp Biocomput 31:251\u2013264","DOI":"10.1142\/9789819824755_0018"},{"key":"6671_CR5","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s00247-023-05746-y","volume":"54","author":"P Ciet","year":"2024","unstructured":"Ciet P, Eade C, Ho ML et al (2024) The unintended consequences of artificial intelligence in paediatric radiology. Pediatr Radiol 54:585\u2013593","journal-title":"Pediatr Radiol"},{"key":"6671_CR6","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1007\/s00247-019-04593-0","volume":"50","author":"N Mahomed","year":"2020","unstructured":"Mahomed N, van Ginneken B, Philipsen R et al (2020) Computer-aided diagnosis for World Health Organization-defined chest radiograph primary-endpoint pneumonia in children. Pediatr Radiol 50:482\u2013491","journal-title":"Pediatr Radiol"},{"key":"6671_CR7","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230227","volume":"6","author":"FC Kitamura","year":"2024","unstructured":"Kitamura FC, Prevedello LM, Colak E et al (2024) Lessons learned in building expertly annotated multi-institution datasets and hosting the RSNA AI challenges. Radiol Artif Intell 6:e230227","journal-title":"Radiol Artif Intell"},{"key":"6671_CR8","doi-asserted-by":"publisher","unstructured":"Wang X, Peng Y, Lu L et al (2017) ChestX-ray8: hospital-scale chest X-ray database and benchmarks for weakly-supervised classification and localization of common thorax diseases. Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) pp\u00a03462\u20133471. https:\/\/doi.org\/10.1109\/cvpr.2017.369","DOI":"10.1109\/cvpr.2017.369"},{"key":"6671_CR9","doi-asserted-by":"publisher","unstructured":"Irvin J, Rajpurkar P, Ko M et al (2019) CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison.\u00a0Proc AAAI Conf Artif Intell 33(1)590\u2013597. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301590","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"6671_CR10","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1038\/s41597-019-0322-0","volume":"6","author":"AEW Johnson","year":"2019","unstructured":"Johnson AEW, Pollard TJ, Berkowitz SJ et al (2019) MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data 6:317","journal-title":"Sci Data"},{"key":"6671_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell\u00a0172:1122\u20131131.e9","journal-title":"Cell"},{"key":"6671_CR12","doi-asserted-by":"publisher","first-page":"3130","DOI":"10.1002\/ppul.27221","volume":"59","author":"C Shi","year":"2024","unstructured":"Shi C, Xu X, Xu Y (2024) Systematic review and meta-analysis of the accuracy of lung ultrasound and chest radiography in diagnosing community acquired pneumonia in children. Pediatr Pulmonol 59:3130\u20133147","journal-title":"Pediatr Pulmonol"},{"key":"6671_CR13","doi-asserted-by":"publisher","DOI":"10.1186\/s13052-024-01583-3","volume":"50","author":"Y Yang","year":"2024","unstructured":"Yang Y, Wu Y, Zhao W (2024) Comparison of lung ultrasound and chest radiography for detecting pneumonia in children: a systematic review and meta-analysis. Ital J Pediatr 50:12","journal-title":"Ital J Pediatr"},{"key":"6671_CR14","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1001\/jamacardio.2024.4991","volume":"10","author":"C Baloescu","year":"2025","unstructured":"Baloescu C, Bailitz J, Cheema B et al (2025) Artificial intelligence-guided lung ultrasound by nonexperts. JAMA Cardiol 10:245\u2013253","journal-title":"JAMA Cardiol"},{"key":"6671_CR15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0309109","volume":"19","author":"D Kessler","year":"2024","unstructured":"Kessler D, Zhu M, Gregory CR et al (2024) Development and testing of a deep learning algorithm to detect lung consolidation among children with pneumonia using hand-held ultrasound. PLoS One 19:e0309109","journal-title":"PLoS ONE"},{"key":"6671_CR16","doi-asserted-by":"crossref","unstructured":"Shea DE, Kulhare S, Millin R et al (2023) Deep learning video classification of lung ultrasound features associated with pneumonia. 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); Vancouver, BC, Canada: IEEE; pp 3103\u201312","DOI":"10.1109\/CVPRW59228.2023.00312"},{"key":"6671_CR17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0206410","volume":"13","author":"M Correa","year":"2018","unstructured":"Correa M, Zimic M, Barrientos F et al (2018) Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition. PLoS One 13:e0206410","journal-title":"PLoS One"},{"key":"6671_CR18","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2020-042547","volume":"11","author":"A Riaz","year":"2021","unstructured":"Riaz A, Cambaco O, Ellington LE et al (2021) Feasibility, usability and acceptability of paediatric lung ultrasound among healthcare providers and caregivers for the diagnosis of childhood pneumonia in resource-constrained settings: a qualitative study. BMJ Open 11:e042547","journal-title":"BMJ Open"},{"key":"6671_CR19","doi-asserted-by":"publisher","unstructured":"Zhang R, Szczykutowicz TP, Toia GV (2025) Artificial intelligence in computed tomography image reconstruction: a review of recent advances. J Comput Assist Tomogr 49(4):521\u2013530. https:\/\/doi.org\/10.1097\/RCT.0000000000001734","DOI":"10.1097\/RCT.0000000000001734"},{"key":"6671_CR20","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M et al (2020) Generative adversarial networks. Commun ACM 63:139\u2013144","journal-title":"Commun ACM"},{"key":"6671_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102846","volume":"88","author":"A Kazerouni","year":"2023","unstructured":"Kazerouni A, Aghdam EK, Heidari M et al (2023) Diffusion models in medical imaging: a comprehensive survey. Med Image Anal 88:102846","journal-title":"Med Image Anal"},{"key":"6671_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100779","volume":"27","author":"S Motamed","year":"2021","unstructured":"Motamed S, Rogalla P, Khalvati F (2021) Data augmentation using generative adversarial networks (GANs) for GAN-based detection of pneumonia and COVID-19 in chest X-ray images. Inform Med Unlocked 27:100779","journal-title":"Inform Med Unlocked"},{"key":"6671_CR23","doi-asserted-by":"publisher","first-page":"781","DOI":"10.2214\/AJR.20.23104","volume":"216","author":"SD Serai","year":"2021","unstructured":"Serai SD, Rapp JB, States LJ et al (2021) Pediatric lung MRI: currently available and emerging techniques. AJR Am J Roentgenol 216:781\u2013790","journal-title":"AJR Am J Roentgenol"},{"key":"6671_CR24","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/JPROC.2022.3141367","volume":"110","author":"Y Chen","year":"2022","unstructured":"Chen Y, Schonlieb CB, Lio P et al (2022) AI-based reconstruction for fast MRI\u2014a systematic review and meta-analysis. Proc IEEE 110:224\u2013245","journal-title":"Proc IEEE"},{"key":"6671_CR25","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.230419","volume":"6","author":"AC Klemenz","year":"2024","unstructured":"Klemenz AC, Reichardt L, Gorodezky M et al (2024) Accelerated cardiac MRI with deep learning-based image reconstruction for cine imaging. Radiol Cardiothorac Imaging 6:e230419","journal-title":"Radiol Cardiothorac Imaging"},{"key":"6671_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/children12040416","volume":"12","author":"A Gearhart","year":"2025","unstructured":"Gearhart A, Anjewierden S, Buddhe S, Tandon A (2025) Review of the current state of artificial intelligence in pediatric cardiovascular magnetic resonance imaging. Children 12:416","journal-title":"Children"},{"key":"6671_CR27","doi-asserted-by":"publisher","first-page":"7615","DOI":"10.1038\/s41467-024-51172-5","volume":"15","author":"EH Lee","year":"2024","unstructured":"Lee EH, Han M, Wright J et al (2024) An international study presenting a federated learning AI platform for pediatric brain tumors. Nat Commun 15:7615","journal-title":"Nat Commun"},{"key":"6671_CR28","doi-asserted-by":"publisher","first-page":"12598","DOI":"10.1038\/s41598-020-69250-1","volume":"10","author":"MJ Sheller","year":"2020","unstructured":"Sheller MJ, Edwards B, Reina GA et al (2020) Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep 10:12598","journal-title":"Sci Rep"},{"key":"6671_CR29","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.csbj.2025.03.031","volume":"28","author":"IN Tzortzis","year":"2025","unstructured":"Tzortzis IN, Gutierrez-Torre A, Sykiotis S et al (2025) Towards generalizable federated learning in medical imaging: a real-world case study on mammography data. Comput Struct Biotechnol J 28:106\u2013117","journal-title":"Comput Struct Biotechnol J"},{"key":"6671_CR30","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1002\/cai2.92","volume":"2","author":"L Lv","year":"2023","unstructured":"Lv L, Zhang Z, Zhang D et al (2023) Machine-learning radiomics to predict bone marrow metastasis of neuroblastoma using magnetic resonance imaging. Cancer Innovation 2:405\u2013415","journal-title":"Cancer Innovation"},{"key":"6671_CR31","doi-asserted-by":"publisher","DOI":"10.1093\/noajnl\/vdac083","volume":"4","author":"R Madhogarhia","year":"2022","unstructured":"Madhogarhia R, Haldar D, Bagheri S et al (2022) Radiomics and radiogenomics in pediatric neuro-oncology: a review. Neuro-Oncology Advances 4:vdac083","journal-title":"Neuro-Oncology Advances"},{"key":"6671_CR32","doi-asserted-by":"publisher","DOI":"10.1101\/2024.06.04.24308434","author":"D Tak","year":"2024","unstructured":"Tak D, Garomsa BA, Zapaishchykova A et al (2024) Longitudinal risk prediction for pediatric glioma with temporal deep learning. medRxiv. https:\/\/doi.org\/10.1101\/2024.06.04.24308434","journal-title":"medRxiv"},{"key":"6671_CR33","unstructured":"AfriDSAI, Emory University HiTi Lab, DSFSI, Pretoria Uo, editors. AfriDSAI Health AI Datathon 2025 \u2013 Programme Brochure. AfriDSAI Health AI Datathon 2025; 2025; Pretoria: University of Pretoria"},{"key":"6671_CR34","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563\u2013577","journal-title":"Radiology"},{"key":"6671_CR35","first-page":"1","volume":"42","author":"VS Viswanathan","year":"2022","unstructured":"Viswanathan VS, Gupta A, Madabhushi A (2022) Novel imaging biomarkers to assess oncologic treatment-related changes. Am Soc Clin Oncol Educ Book 42:1\u201313","journal-title":"Am Soc Clin Oncol Educ Book"},{"key":"6671_CR36","doi-asserted-by":"publisher","first-page":"2333","DOI":"10.3390\/diagnostics13142333","volume":"13","author":"G Rea","year":"2023","unstructured":"Rea G, Sverzellati N, Bocchino M et al (2023) Beyond visual interpretation: quantitative analysis and artificial intelligence in interstitial lung disease diagnosis \u201cExpanding Horizons in Radiology.\u201d Diagnostics (Basel) 13:2333","journal-title":"Diagnostics (Basel)"},{"key":"6671_CR37","doi-asserted-by":"publisher","first-page":"642","DOI":"10.2214\/AJR.21.26682","volume":"218","author":"W Kim","year":"2022","unstructured":"Kim W, Lee SM, Kim JI et al (2022) Utility of a deep learning algorithm for detection of reticular opacity on chest radiography in patients with interstitial lung disease. AJR Am J Roentgenol 218:642\u2013650","journal-title":"AJR Am J Roentgenol"},{"key":"6671_CR38","doi-asserted-by":"publisher","first-page":"236","DOI":"10.3390\/jimaging9110236","volume":"9","author":"JM Farina","year":"2023","unstructured":"Farina JM, Pereyra M, Mahmoud AK et al (2023) Artificial intelligence-based prediction of cardiovascular diseases from chest radiography. J Imaging 9:236","journal-title":"J Imaging"},{"key":"6671_CR39","doi-asserted-by":"publisher","first-page":"e525","DOI":"10.1016\/S2589-7500(23)00107-3","volume":"5","author":"D Ueda","year":"2023","unstructured":"Ueda D, Matsumoto T, Ehara S et al (2023) Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multi-institutional, retrospective model development and validation study. Lancet Digit Health 5:e525\u2013e533","journal-title":"Lancet Digit Health"},{"key":"6671_CR40","doi-asserted-by":"publisher","DOI":"10.1177\/21501319251353372","author":"NR Chinapuvvula","year":"2025","unstructured":"Chinapuvvula NR, Hatley M, Khan R et al (2025) Opportunistic use of chest X-ray for identifying older adults at risk of osteoporosis and not meeting criteria for screening. J Prim Care Community Health. https:\/\/doi.org\/10.1177\/21501319251353372","journal-title":"J Prim Care Community Health"},{"key":"6671_CR41","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.222937","volume":"308","author":"K Xu","year":"2023","unstructured":"Xu K, Khan MS, Li TZ, Gao R, Terry JG, Huo Y et al (2023) AI body composition in lung cancer screening: added value beyond lung cancer detection. Radiology 308:e222937","journal-title":"Radiology"},{"key":"6671_CR42","doi-asserted-by":"publisher","DOI":"10.3390\/info16070591","volume":"16","author":"B Jandoubi","year":"2025","unstructured":"Jandoubi B, Akhloufi MA (2025) Multimodal artificial intelligence in medical diagnostics. Information (Basel) 16:591","journal-title":"Information (Basel)"},{"key":"6671_CR43","doi-asserted-by":"publisher","DOI":"10.2196\/67144","volume":"4","author":"M Rahman","year":"2025","unstructured":"Rahman M, Gao J, Carey KA et al (2025) Comparison of deep learning approaches using chest radiographs for predicting clinical deterioration: retrospective observational study. JMIR AI 4:e67144","journal-title":"JMIR AI"},{"key":"6671_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102949","volume":"156","author":"E Pachetti","year":"2024","unstructured":"Pachetti E, Colantonio S (2024) A systematic review of few-shot learning in medical imaging. Artif Intell Med 156:102949","journal-title":"Artif Intell Med"},{"key":"6671_CR45","unstructured":"Cheng S, Starosolski ZA, Subramanian D (2024) Self-supervised learning for building robust pediatric chest X-ray classification models.\u00a0arXiv:2409.00231\u00a0"},{"key":"6671_CR46","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0308758","volume":"19","author":"EI I","year":"2024","unstructured":"Ihongbe I, Fouad S, Mahmoud TF, et al (2024) Evaluating explainable artificial intelligence (XAI) techniques in chest radiology imaging through a human-centered lens. PLoS One 19:e0308758","journal-title":"PLoS ONE"},{"key":"6671_CR47","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.233261","volume":"313","author":"D Prinster","year":"2024","unstructured":"Prinster D, Mahmood A, Saria S et al (2024) Care to explain? AI explanation types differentially impact chest radiograph diagnostic performance and physician trust in AI. Radiology 313:e233261","journal-title":"Radiology"},{"key":"6671_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(25)00202-8","volume":"405","author":"J Zou","year":"2025","unstructured":"Zou J, Topol EJ (2025) The rise of agentic AI teammates in medicine. Lancet 405:457","journal-title":"Lancet"},{"key":"6671_CR49","volume":"17","author":"HN Jamil Abusamra","year":"2025","unstructured":"Jamil Abusamra HN, Ali SHM, Khidir Elhussien WA et al (2025) Ethical and practical considerations of artificial intelligence in pediatric medicine: a systematic review. Cureus 17:e79024","journal-title":"Cureus"},{"key":"6671_CR50","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13172760","volume":"13","author":"R Najjar","year":"2023","unstructured":"Najjar R (2023) Redefining radiology: a review of artificial intelligence integration in medical imaging. Diagnostics (Basel) 13:2760","journal-title":"Diagnostics (Basel)"},{"key":"6671_CR51","doi-asserted-by":"publisher","first-page":"2100","DOI":"10.1007\/s00247-021-05195-5","volume":"52","author":"SC Shelmerdine","year":"2022","unstructured":"Shelmerdine SC, Rosendahl K, Arthurs OJ (2022) Artificial intelligence in paediatric radiology: international survey of health care professional opinions.\u00a0Pediatr Radiol 52:30\u201341","journal-title":"Pediatr Radiol"}],"container-title":["Pediatric Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00247-026-06671-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00247-026-06671-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00247-026-06671-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T09:21:10Z","timestamp":1788859270000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00247-026-06671-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,3]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["6671"],"URL":"https:\/\/doi.org\/10.1007\/s00247-026-06671-6","relation":{},"ISSN":["1432-1998"],"issn-type":[{"value":"1432-1998","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,3]]},"assertion":[{"value":"10 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Artificial intelligence\u2013assisted tools were used to support language editing, structural refinement and drafting of phrasing. All scientific statements, interpretations and conclusions were developed and verified by the authors using the cited literature and original clinical expertise. All AI-assisted suggestions were critically reviewed and edited, and the authors take full responsibility for the accuracy and integrity of the final manuscript.","order":1,"name":"Ethics","label":"Use of artificial intelligence","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"this article does not contain any studies with human participants or animals performed by any of  the authors.","order":2,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"None","order":3,"name":"Ethics","label":"Conflicts of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}