Artificial intelligence is emerging as a powerful tool to bridge a major gap in pediatric heart care, especially for children in low- and middle-income countries where access to specialized cardiology services is limited. At Boston Children’s Hospital, Drs. John Triedman, Sunil Ghelani, and Joshua Mayourian have launched the Congenital Heart Artificial Intelligence (CHAI) Lab, pioneering efforts to use AI to improve diagnosis and treatment of congenital heart disease worldwide. One of the biggest challenges in global pediatric cardiology is the lack of access to advanced diagnostic tools. In many regions, children with heart conditions go undiagnosed or receive delayed care—studies suggest up to 90% face significant barriers to proper treatment. The CHAI Lab aims to change that by developing AI tools that can analyze routine, widely available data, such as electrocardiograms (ECGs), to detect heart problems early. ECGs are simple, low-cost, and easy to perform, making them ideal for use in resource-limited settings. The CHAI Lab has created AI models that go beyond basic rhythm analysis, uncovering subtle patterns in ECG signals that may indicate structural heart issues or impaired heart function. In one study published in JACC: Clinical Electrophysiology, the lab’s AI-ECG model outperformed commercial software and even matched or exceeded expert cardiologists in identifying serious conditions like Wolff-Parkinson-White syndrome and long QT syndrome—both of which can lead to sudden cardiac arrest. Perhaps most striking is the lab’s ability to detect ventricular dysfunction—a condition where the heart doesn’t pump effectively—using only ECG data. The AI identifies minute changes in the QRS complex, a key part of the ECG, that reflect abnormal electrical activation in the heart muscle. These signals are often too subtle for human experts to notice, but AI can pick them up consistently, enabling earlier intervention. By leveraging decades of anonymized patient data from Boston Children’s extensive medical records—some dating back over 60 years—the lab trains its models on a diverse and rich dataset. This includes detailed clinical histories, imaging results, and treatment outcomes, all organized through a unique coding system that enhances data analysis. The result is AI that learns not just from isolated cases, but from real-world patterns of care and long-term outcomes. The goal is not to replace doctors, but to empower them—especially in areas with few pediatric cardiologists. With AI-ECG tools, even general practitioners or nurses could screen children during routine visits, such as school sports physicals, and flag those who need urgent referral. This could dramatically expand access to early diagnosis and life-saving treatment. The CHAI Lab is committed to responsible AI development, testing its models across diverse populations and gathering feedback from clinicians and patients to build trust and ensure fairness. By sharing these tools globally, the team hopes to democratize access to advanced heart care knowledge, turning decades of clinical expertise into scalable solutions that can save lives far beyond the walls of Boston Children’s Hospital.