Emerging Applications AI is being increasingly integrated into clinical workflows in pediatric cardiology. Tools have been developed for automated ECG interpretation, detection of inherited arrhythmia syndromes, segmentation of echocardiographic images, and outcome prediction in congenital heart disease. One recent example is the application of deep learning models to pediatric Apple Watch tracings, which showed diagnostic performance on par with human experts (1, 2).

Data-Driven Advances High-dimensional datasets, including genomic panels, wearable sensor data, and multi-frame imaging, can now be processed using machine learning to uncover patterns previously invisible to human interpretation. In pediatric echocardiography, explainable AI models have recently demonstrated clinical utility in real-time image classification, even in neonates and infants (3).

From Assistance to Autonomy Traditionally used as diagnostic aids, some AI tools are now exceeding human performance. In a groundbreaking study, AI outperformed clinicians in classifying ECG arrhythmias and predicting one-year mortality—even from normal-appearing tracings (4, 5). This trajectory raises fundamental questions about the evolving role of clinicians in pediatric care.

Digital Twin: Promise and Pressure The concept of the digital twin—a virtual model integrating real-time physiological, behavioral, and genetic data—offers immense potential. In children with congenital or acquired heart disease, it could enable simulation-based personalized therapies, continuous risk monitoring, and adaptive treatment planning (6).

Pediatric-Specific Risks Unlike adults, children's developmental trajectories, dependency relationships, and legal consent structures make digital twin deplopment ethically fraught. Predictive modeling across decades could lead to anticipatory discrimination in education or insurance, amplify parental anxiety, and reduce the child's future autonomy. Such risks demand age-specific safeguards.

Pediatric Data Sensitivity—Children's data are vulnerable not only because of identifiability but due to the lifelong implications of predictive labels. Models that assign future disease risk—even with high accuracy—can unintentionally shape identity, expectations, and healthcare access.

Regulatory Lag and Clinical Uncertainty—While the EU AI Act provides a framework for high-risk AI systems (7), it lacks pediatric-specific stipulations. Regulatory authorities must address critical issues such as the minimum age for data inclusion, dynamic consent models for growing children, and the long-term governance of digital twins.

The Changing Physician Role—As AI systems assume more diagnostic responsibility, clinicians may become safety overseers rather than decision-makers. This change challenges traditional models of accountability and requires rethinking medical education, trust building with families, and shared decision-making paradigms.