{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T08:51:43Z","timestamp":1791449503089,"version":"4.3.3"},"reference-count":28,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Cardiol Young"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"S1047951126124342_as1\">\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>Artificial intelligence is increasingly applied in paediatric cardiology and cardiac intensive care, where CHD, postoperative physiologic instability, and continuous bedside monitoring generate complex datasets. This review summarises current applications of artificial intelligence in paediatric cardiology and the cardiac ICU, focusing on cardiac imaging, electrocardiography, predictive analytics, waveform intelligence, and postoperative outcome prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1047951126124342_as2\">\n                    <jats:title>Data sources:<\/jats:title>\n                    <jats:p>A narrative review of PubMed, MEDLINE, and Scopus was performed for studies published from 2018 through 2026 evaluating artificial intelligence, machine learning, or deep learning in paediatric cardiology, CHD, congenital heart surgery, and cardiac critical care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1047951126124342_as3\">\n                    <jats:title>Study selection:<\/jats:title>\n                    <jats:p>Studies addressing paediatric or CHD-focused applications in echocardiography, cardiac MRI, electrocardiography, cardiac ICU monitoring, waveform analytics, or postoperative complication prediction were included, together with relevant reviews.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1047951126124342_as4\">\n                    <jats:title>Data extraction:<\/jats:title>\n                    <jats:p>Information was extracted on study population, artificial intelligence method, clinical task, model inputs, outcomes, validation strategy, and major limitations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1047951126124342_as5\">\n                    <jats:title>Data synthesis:<\/jats:title>\n                    <jats:p>Artificial intelligence is most developed in imaging and electrocardiography, while paediatric and complex CHD evidence remains less mature. In cardiac ICU, models using electronic health record variables, high-frequency physiologic data, and risk indices have been evaluated for cardiac arrest, low cardiac output physiology, hyperlactatemia, extubation failure, and vasoactive de-escalation. External validation, prospective deployment, calibration, and workflow-safe implementation remain major gaps.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1047951126124342_as6\">\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Artificial intelligence may improve diagnostic consistency, risk stratification, and early recognition of deterioration, but prospective validation and measurable clinical benefit are needed before autonomous use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1017\/s1047951126124342","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T08:12:08Z","timestamp":1791447128000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in paediatric cardiac critical care: from predictive analytics to continuous physiologic intelligence"],"prefix":"10.1017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4331-4061","authenticated-orcid":false,"given":"Fabio","family":"Savorgnan","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02pttbw34","id-type":"ROR","asserted-by":"publisher"}],"name":"Baylor College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pranathi","family":"Pilla","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohit","family":"Loomba","sequence":"additional","affiliation":[{"name":"Northwestern University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6274-5775","authenticated-orcid":false,"given":"Saul","family":"Flores","sequence":"additional","affiliation":[{"name":"Baylor College of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"S1047951126124342_ref14","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000001201"},{"key":"S1047951126124342_ref4","unstructured":"4. Sciencedirect review on AI in pediatric cardiology \/ critical care context. Pediatr Neonatol \/ related indexed review source. 2025."},{"key":"S1047951126124342_ref9","first-page":"416","article-title":"Review of the current state of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging","volume":"12","author":"Gearhart","year":"2025","journal-title":"Children (Basel)"},{"key":"S1047951126124342_ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacep.2025.02.003"},{"key":"S1047951126124342_ref15","first-page":"e1327","article-title":"Supervised Machine Learning models predicting postoperative low cardiac output syndrome in neonates","volume":"7","author":"Baloglu","year":"2025","journal-title":"Crit Care Explor"},{"key":"S1047951126124342_ref18","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehad655.061"},{"key":"S1047951126124342_ref20","unstructured":"20. Mayo Clinic pediatric cardiology AI-ECG report on pediatric ventricular dysfunction detection. 2025."},{"key":"S1047951126124342_ref2","first-page":"14","article-title":"Artificial Intelligence in fetal and pediatric echocardiography","volume":"12","author":"Wang","year":"2024","journal-title":"Children (Basel)"},{"key":"S1047951126124342_ref19","doi-asserted-by":"publisher","DOI":"10.21037\/pm-24-72"},{"key":"S1047951126124342_ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01342-5"},{"key":"S1047951126124342_ref16","doi-asserted-by":"publisher","DOI":"10.1097\/JS9.0000000000001112"},{"key":"S1047951126124342_ref17","doi-asserted-by":"publisher","DOI":"10.1002\/uog.27503"},{"key":"S1047951126124342_ref22","first-page":"128","article-title":"From images to interventions: AI-driven echocardiographic decision-making in pediatric critical heart diseases","volume":"8","author":"Abdel-Wahab","year":"2025","journal-title":"Vasc Endovasc Rev"},{"key":"S1047951126124342_ref10","doi-asserted-by":"publisher","DOI":"10.1186\/s12968-018-0471-x"},{"key":"S1047951126124342_ref23","doi-asserted-by":"publisher","DOI":"10.1017\/S1047951121003231"},{"key":"S1047951126124342_ref6","doi-asserted-by":"publisher","DOI":"10.3389\/fped.2025.1581578"},{"key":"S1047951126124342_ref3","first-page":"25","article-title":"Artificial Intelligence in pediatric electrocardiography: a comprehensive review","volume":"12","author":"Leone","year":"2024","journal-title":"Children (Basel)"},{"key":"S1047951126124342_ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2145-8"},{"key":"S1047951126124342_ref24","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehy915"},{"key":"S1047951126124342_ref26","doi-asserted-by":"publisher","DOI":"10.1097\/PCC.0000000000003703"},{"key":"S1047951126124342_ref28","first-page":"e0600","article-title":"Low inadequate oxygen delivery index is associated with decreased cardiac arrest risk in high-risk pediatric ICU patients","volume":"4","author":"Dewan","year":"2022","journal-title":"Crit Care Explor"},{"key":"S1047951126124342_ref13","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12072728"},{"key":"S1047951126124342_ref21","doi-asserted-by":"publisher","DOI":"10.4103\/apc.apc_72_24"},{"key":"S1047951126124342_ref5","doi-asserted-by":"publisher","DOI":"10.1186\/s12872-024-04336-6"},{"key":"S1047951126124342_ref11","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0268-3"},{"key":"S1047951126124342_ref27","first-page":"e0563","article-title":"Use of a risk analytic algorithm to inform weaning from vasoactive medication in patients following pediatric cardiac surgery","volume":"3","author":"Goldsmith","year":"2021","journal-title":"Crit Care Explor"},{"key":"S1047951126124342_ref1","first-page":"403","article-title":"Role of Artificial Intelligence in Congenital Heart Disease","volume":"27","author":"Holt","year":"2025","journal-title":"Curr Cardiol Rep"},{"key":"S1047951126124342_ref25","first-page":"e1013","article-title":"A near real-time risk analytics algorithm predicts elevated lactate levels in pediatric cardiac critical care patients","volume":"5","author":"Asfari","year":"2023","journal-title":"Crit Care Explor"}],"container-title":["Cardiology in the Young"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S1047951126124342","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T08:12:10Z","timestamp":1791447130000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S1047951126124342\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,8]]},"references-count":28,"alternative-id":["S1047951126124342"],"URL":"https:\/\/doi.org\/10.1017\/s1047951126124342","relation":{},"ISSN":["1047-9511","1467-1107"],"issn-type":[{"value":"1047-9511","type":"print"},{"value":"1467-1107","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,8]]},"assertion":[{"value":"\u00a9 The Author(s), 2026. Published by Cambridge University Press","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}}]}}