{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T05:02:53Z","timestamp":1784869373659,"version":"3.55.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T00:00:00Z","timestamp":1780704000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T00:00:00Z","timestamp":1780704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Indian J Pediatr"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1007\/s12098-026-06254-1","type":"journal-article","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T11:53:18Z","timestamp":1780746798000},"page":"840-845","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Emerging Applications of Artificial Intelligence in Pediatric Care"],"prefix":"10.1007","volume":"93","author":[{"given":"Thimiri Palani","family":"Murugan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sutharson","family":"Ramasamy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kurien Anil","family":"Kuruvilla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,6]]},"reference":[{"key":"6254_CR1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1542\/hpeds.2021-006094","volume":"12","author":"CO Adegboro","year":"2022","unstructured":"Adegboro CO, Choudhury A, Asan O, Kelly MM. Artificial intelligence to improve health outcomes in the NICU and PICU: a systematic review. Hosp Pediatr. 2022;12:93\u2013110.","journal-title":"Hosp Pediatr"},{"key":"6254_CR2","first-page":"e79024","volume":"17","author":"HN Jamil Abusamra","year":"2025","unstructured":"Jamil Abusamra HN, Ali SHM, Khidir Elhussien WA, Ahmed Mirghani AM, Alameen Ahmed AA, Abdelrahman Ibrahim ME. Ethical and practical considerations of artificial intelligence in pediatric medicine: a systematic review. Cureus. 2025;17:e79024.","journal-title":"Cureus"},{"key":"6254_CR3","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/s13312-023-2936-8","volume":"60","author":"Y Balla","year":"2023","unstructured":"Balla Y, Tirunagari S, Windridge D. Pediatrics in artificial intelligence era: a systematic review on challenges, opportunities, and explainability. Indian Pediatr. 2023;60:561\u20139.","journal-title":"Indian Pediatr"},{"key":"6254_CR4","doi-asserted-by":"publisher","DOI":"10.1101\/2022.10.07.22280541","author":"X Ge","year":"2022","unstructured":"Ge X, Wang Y, Xie L, et al. development and validation of a deep learning system for the diagnosis of pediatric diseases: a large-scale real-world data study in Shanghai. medRxiv. 2022. https:\/\/doi.org\/10.1101\/2022.10.07.22280541.","journal-title":"medRxiv"},{"key":"6254_CR5","doi-asserted-by":"crossref","unstructured":"Iglovikov VI, Rakhlin A, Kalinin AA, Shvets AA. Pediatric bone age assessment using deep convolutional neural networks. In: Stoyanov D, Taylor Z, Carneiro G, et al. editors. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Cham: Springer; 2018. pp. 300\u20138.","DOI":"10.1101\/234120"},{"key":"6254_CR6","first-page":"225","volume":"27","author":"E Kaya","year":"2024","unstructured":"Kaya E, G\u00fcne\u00e7 HG, \u00dcrkmez E\u015e, Ayd\u0131n KC, Fehmi H. Deep learning for diagnostic charting on pediatric panoramic radiographs. Int J Comput Dent. 2024;27:225\u201333.","journal-title":"Int J Comput Dent"},{"key":"6254_CR7","first-page":"1208","volume":"5","author":"S Ramesh","year":"2021","unstructured":"Ramesh S, Chokkara S, Shen T, et al. Applications of artificial intelligence in pediatric oncology: a systematic review. JCO Clin Cancer Inf. 2021;5:1208\u201319.","journal-title":"JCO Clin Cancer Inf"},{"key":"6254_CR8","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44\u201356.","journal-title":"Nat Med"},{"key":"6254_CR9","doi-asserted-by":"publisher","DOI":"10.33590\/emjinnov\/19-00172","author":"MP Sendak","year":"2020","unstructured":"Sendak MP, D\u2019Arcy J, Kashyap S, et al. A path for translation of machine learning products into healthcare delivery. EMJ Innov. 2020. https:\/\/doi.org\/10.33590\/emjinnov\/19-00172.","journal-title":"EMJ Innov"},{"key":"6254_CR10","doi-asserted-by":"publisher","DOI":"10.1101\/2025.03.25.25324127","author":"K Cao","year":"2025","unstructured":"Cao K, Braykov N, McCarter A, et al. Development and validation of an artificial intelligence predictive model to accelerate antibiotic therapy for critical ill children with sepsis in the pediatric ED with pediatric ICU disposition. medRxiv. 2025. https:\/\/doi.org\/10.1101\/2025.03.25.25324127.","journal-title":"medRxiv"},{"key":"6254_CR11","doi-asserted-by":"publisher","first-page":"e516","DOI":"10.1097\/PCC.0000000000002101","volume":"20","author":"M Eisenberg","year":"2019","unstructured":"Eisenberg M, Madden K, Christianson JR, Melendez E, Harper MB. Performance of an automated screening algorithm for early detection of pediatric severe sepsis. Pediatr Crit Care Med. 2019;20:e516\u201323.","journal-title":"Pediatr Crit Care Med"},{"key":"6254_CR12","doi-asserted-by":"publisher","first-page":"921884","DOI":"10.3389\/fphys.2022.921884","volume":"13","author":"P Singh","year":"2022","unstructured":"Singh P, Nagori A, Lodha R, Sethi T. Early prediction of hypothermia in pediatric intensive care units using machine learning. Front Physiol. 2022;13:921884.","journal-title":"Front Physiol"},{"key":"6254_CR13","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s12245-025-00861-z","volume":"18","author":"MA Halwani","year":"2025","unstructured":"Halwani MA, Merdad G, Almasre M, et al. Predicting triage of pediatric patients in the emergency department using machine learning approach. Int J Emerg Med. 2025;18:51.","journal-title":"Int J Emerg Med"},{"key":"6254_CR14","first-page":"e80142","volume":"17","author":"ABA Abdalwahab Abdallah","year":"2025","unstructured":"Abdalwahab Abdallah ABA, Hafez Sadaka SI, Ali AI, et al. The role of artificial intelligence in pediatric intensive care: a systematic review. Cureus. 2025;17:e80142.","journal-title":"Cureus"},{"key":"6254_CR15","doi-asserted-by":"publisher","first-page":"e257831","DOI":"10.1001\/jamanetworkopen.2025.7831","volume":"8","author":"A Ortiz","year":"2025","unstructured":"Ortiz A, Pati\u00f1o S, Torres J, et al. AI-enabled screening for retinopathy of prematurity in low-resource settings. JAMA Netw Open. 2025;8:e257831.","journal-title":"JAMA Netw Open"},{"key":"6254_CR16","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1007\/s10462-025-11153-6","volume":"58","author":"A Jafarizadeh","year":"2025","unstructured":"Jafarizadeh A, Maleki SF, Pouya P, et al. Current and future roles of artificial intelligence in retinopathy of prematurity. Artif Intell Rev. 2025;58:188.","journal-title":"Artif Intell Rev"},{"key":"6254_CR17","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.3390\/biomedinformatics4020067","volume":"4","author":"D Rallis","year":"2024","unstructured":"Rallis D, Baltogianni M, Kapetaniou K, Giapros V. Current applications of artificial intelligence in the neonatal intensive care unit. BioMedInformatics. 2024;4:1225\u201348.","journal-title":"BioMedInformatics"},{"key":"6254_CR18","first-page":"e88212","volume":"17","author":"DSR A V R","year":"2025","unstructured":"A V R, D SR, Bantu P, Kotha R. AI-driven neonatal MRI interpretation: a systematic review of diagnostic efficiency, prognostic value, and implementation barriers for hypoxic-ischemic encephalopathy. Cureus. 2025;17:e88212.","journal-title":"Cureus"},{"key":"6254_CR19","doi-asserted-by":"publisher","first-page":"11872","DOI":"10.1038\/s41598-022-16066-w","volume":"12","author":"MW Wagner","year":"2022","unstructured":"Wagner MW, So D, Guo T, et al. MRI based radiomics enhances prediction of neurodevelopmental outcome in very preterm neonates. Sci Rep. 2022;12:11872.","journal-title":"Sci Rep"},{"key":"6254_CR20","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.3390\/app13053211","volume":"13","author":"R Chioma","year":"2023","unstructured":"Chioma R, Sbordone A, Patti ML, Perri A, Vento G, Nobile S. Applications of artificial intelligence in neonatology. Appl Sci. 2023;13:3211.","journal-title":"Appl Sci"},{"key":"6254_CR21","doi-asserted-by":"publisher","first-page":"151992","DOI":"10.1016\/j.semperi.2024.151992","volume":"48","author":"RM Fortunov","year":"2024","unstructured":"Fortunov RM, Cabacungan E, Barry JS, Jagarapu J. Artificial intelligence and informatics in neonatal resuscitation. Semin Perinatol. 2024;48:151992.","journal-title":"Semin Perinatol"},{"key":"6254_CR22","doi-asserted-by":"publisher","first-page":"101333","DOI":"10.1016\/j.siny.2022.101333","volume":"27","author":"SJE Cramer","year":"2022","unstructured":"Cramer SJE, Kuypers KLAM, Martherus T, Dekker J, Te Pas AB. Technology in the delivery room supporting the neonatal healthcare provider\u2019s task. Semin Fetal Neonatal Med. 2022;27:101333.","journal-title":"Semin Fetal Neonatal Med"},{"key":"6254_CR23","doi-asserted-by":"publisher","first-page":"e2450260","DOI":"10.1001\/jamanetworkopen.2024.50260","volume":"7","author":"AJH Ngeow","year":"2024","unstructured":"Ngeow AJH, Moosa AS, Tan MG, et al. BiliSG Study Group. Development and validation of a smartphone application for neonatal jaundice screening. JAMA Netw Open. 2024;7:e2450260.","journal-title":"JAMA Netw Open"},{"key":"6254_CR24","doi-asserted-by":"publisher","first-page":"105538","DOI":"10.1016\/j.earlhumdev.2022.105538","volume":"165","author":"J Guedalia","year":"2022","unstructured":"Guedalia J, Farkash R, Wasserteil N, et al. Primary risk stratification for neonatal jaundice among term neonates using machine learning algorithm. Early Hum Dev. 2022;165:105538.","journal-title":"Early Hum Dev"},{"key":"6254_CR25","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1038\/s41390-022-02322-2","volume":"93","author":"A Sitek","year":"2023","unstructured":"Sitek A, Seliga-Siwecka J, P\u0142otka S, et al. Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns. Pediatr Res. 2023;93:376\u201381.","journal-title":"Pediatr Res"},{"key":"6254_CR26","doi-asserted-by":"publisher","first-page":"e47612","DOI":"10.2196\/47612","volume":"25","author":"W Jang","year":"2023","unstructured":"Jang W, Choi YS, Kim JY, et al. Artificial intelligence-driven respiratory distress syndrome prediction for very low birth weight infants: Korean multicenter prospective cohort study. J Med Internet Res. 2023;25:e47612.","journal-title":"J Med Internet Res"},{"key":"6254_CR27","first-page":"e140115","volume":"14","author":"LQ Bui","year":"2025","unstructured":"Bui LQ, Kadam SJ. Role of artificial intelligence in earlier diagnosis of bronchopulmonary dysplasia. J Pediatr Neonat Individual Med. 2025;14:e140115.","journal-title":"J Pediatr Neonat Individual Med"},{"key":"6254_CR28","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1159\/000516891","volume":"118","author":"C Mangold","year":"2021","unstructured":"Mangold C, Zoretic S, Thallapureddy K, Moreira A, Chorath K, Moreira A. Machine learning models for predicting neonatal mortality: a systematic review. Neonatology. 2021;118:394\u2013405.","journal-title":"Neonatology"},{"key":"6254_CR29","doi-asserted-by":"publisher","first-page":"ckaf161","DOI":"10.1093\/eurpub\/ckaf161.1930","volume":"35","author":"M Ferrante","year":"2025","unstructured":"Ferrante M, Vecchio C, Rapisarda P, Conti G. Use of artificial intelligence to improve perinatal mental health: a systematic review. Eur J Public Health. 2025;35:ckaf161\u20131930.","journal-title":"Eur J Public Health"},{"key":"6254_CR30","doi-asserted-by":"publisher","first-page":"1112074","DOI":"10.3389\/fped.2023.1112074","volume":"11","author":"KA Mc Cord-De Iaco","year":"2023","unstructured":"Mc Cord-De Iaco KA, Gesualdo F, Pandolfi E, Croci I, Tozzi AE. Machine learning clinical decision support systems for surveillance: a case study on pertussis and RSV in children. Front Pediatr. 2023;11:1112074.","journal-title":"Front Pediatr"},{"key":"6254_CR31","doi-asserted-by":"publisher","first-page":"6638","DOI":"10.3390\/app14156638","volume":"14","author":"SY Kim","year":"2024","unstructured":"Kim SY, Kim DH, Kim MJ, Ko HJ, Jeong OR. XAI-based clinical decision support systems: a systematic review. Appl Sci. 2024;14:6638.","journal-title":"Appl Sci"},{"key":"6254_CR32","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1186\/s12882-021-02459-y","volume":"22","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Zheng X, Wang J, et al. Effect of clinical decision support systems on clinical outcome for acute kidney injury: a systematic review and meta-analysis. BMC Nephrol. 2021;22:271.","journal-title":"BMC Nephrol"},{"key":"6254_CR33","doi-asserted-by":"publisher","first-page":"807","DOI":"10.3390\/jcm14030807","volume":"14","author":"HA Ganatra","year":"2025","unstructured":"Ganatra HA. Machine learning in pediatric healthcare: current trends, challenges, and future directions. J Clin Med. 2025;14:807.","journal-title":"J Clin Med"},{"key":"6254_CR34","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1038\/s41390-022-02226-1","volume":"93","author":"S Ramgopal","year":"2023","unstructured":"Ramgopal S, Sanchez-Pinto LN, Horvat CM, Carroll MS, Luo Y, Florin TA. Artificial intelligence-based clinical decision support in pediatrics. Pediatr Res. 2023;93:334\u201341.","journal-title":"Pediatr Res"}],"container-title":["Indian Journal of Pediatrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12098-026-06254-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12098-026-06254-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12098-026-06254-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T04:35:10Z","timestamp":1784867710000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12098-026-06254-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,6]]},"references-count":34,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["6254"],"URL":"https:\/\/doi.org\/10.1007\/s12098-026-06254-1","relation":{},"ISSN":["0019-5456","0973-7693"],"issn-type":[{"value":"0019-5456","type":"print"},{"value":"0973-7693","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,6]]},"assertion":[{"value":"8 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"None.","order":1,"name":"Ethics","label":"Conflict of Interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}