Artificial Intelligence in Pediatric Cardiac Intensive Care: Clinical Applications, Implementation Challenges, and Future Directions

Purpose of Review: Pediatric cardiac intensive care units manage high risk, rapidly evolving physiology in children with congenital heart disease within a data dense environment shaped by continuous monitoring, device-based therapies, and time constrained decisions. This review summarizes how artificial intelligence (AI) is being applied to clinically meaningful pediatric cardiac intensive care unit (PCICU) problems and outlines the implementation barriers and future directions required for safe bedside translation. Recent Findings: AI enabled decision support in PCICU has expanded across short horizon early warning systems, including cardiac arrest and hemodynamic decompensation syndromes such as low cardiac output syndrome, dynamic extubation readiness assessment, and prediction of postoperative complications such as cardiac surgery associated acute kidney injury. These advances are increasingly supported by high fidelity physiologic data infrastructures and interpretability tools that can link risk outputs to recognizable physiologic patterns and therapy intensity. In parallel, digital twin approaches in congenital heart disease, spanning electrophysiology focused and anatomy centered models, are emerging as a pathway toward simulation enabled personalization. Despite this momentum, most studies remain retrospective, with limited external validation, infrequent calibration reporting, and persistent gaps in prospective impact evaluation and workflow integration. Key barriers include pediatric data scarcity, center specific bias, alarm ecology and usability constraints, governance and lifecycle monitoring requirements, and regulatory and financial bottlenecks for pediatric specific AI. Summary: AI is poised to mature from isolated prediction models into trustworthy clinical infrastructure for PCICU, but clinical impact will depend on implementation discipline as much as model performance. High priority needs include multicenter collaboration, including privacy preserving scaling strategies such as federated learning, harmonized definitions and time synchronization standards for high fidelity streams, human centered and human in the loop design to reduce alert burden, routine subgroup auditing to mitigate inequities, and protocol linked actionability that maps risk states to predefined responses. With robust governance, transparency, and clinician education, AI can enhance intensive care unit to home continuity and personalized care while preserving safety, accountability, and patient clinician trust.

Duke Scholars

Author Bradley Marino Pediatrics, Cardiology