Artificial intelligence in paediatric cardiac critical care: from predictive analytics to continuous physiologic intelligence.

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Abstract

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. 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. 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. Information was extracted on study population, artificial intelligence method, clinical task, model inputs, outcomes, validation strategy, and major limitations. 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. 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.

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