Leveraging AI to Bridge Gaps in Pediatric Heart Care Globally

Recent advancements in artificial intelligence are paving the way for significant improvements in pediatric cardiovascular healthcare, especially in underserved regions worldwide. In many low- and middle-income countries, a substantial number of children with congenital heart defects do not receive adequate diagnosis and treatment primarily due to limited access to advanced diagnostic technologies. Studies indicate that up to 90% of children in these regions have restricted access to proper heart care, highlighting a critical healthcare disparity.

To combat this issue, a pioneering initiative at Boston Children's Hospital has established the Congenital Heart Artificial Intelligence (CHAI) Lab. This collaborative effort brings together cardiologists, computer scientists, and data specialists to develop AI tools tailored for diagnosing and managing pediatric heart conditions. Dr. Joshua Mayourian, a cardiology fellow, explains that while Boston-based clinics have ample resources to deliver high-quality care, many other parts of the world lack such capabilities. The AI tools aim to help local cardiologists identify early signs of heart disease, enabling timely intervention.

One of the key projects involves applying AI to electrocardiograms (ECGs), a widely available, cost-effective diagnostic test that records the electrical activity of the heart. Though basic, ECGs contain a wealth of information, and using AI, researchers can analyze data to assess heart rhythm, pumping function, and structural abnormalities. Preliminary studies show that AI-enhanced ECG analysis can outperform traditional software in detecting serious conditions like Wolff-Parkinson-White syndrome and long QT syndrome, both of which pose risks of sudden cardiac arrest.

Furthermore, AI models can identify subtle signals indicating ventricular dysfunction—an impairment of the heart’s pumping ability—by analyzing the QRS complex in ECGs. This capability enables detection of more complex cardiac issues that even experienced physicians might overlook.

The simplicity and affordability of ECG machines make this technology particularly promising for resource-limited settings. AI-driven ECG analysis could facilitate large-scale screenings, such as during school sports physicals, and prioritize children who urgently need medical attention. This approach helps optimize limited healthcare resources and extend the reach of specialized care.

The CHAI Lab benefits from Boston Children's extensive, anonymized medical database spanning over six decades, which enhances the training and validation of AI models. These models can learn from a broad spectrum of cases, improving diagnostic accuracy and understanding of congenital heart diseases over time.

To ensure the safe and ethical application of AI tools, the team emphasizes diverse population testing and ongoing feedback from medical professionals and patients, fostering trust in these innovations. Ultimately, the goal is to democratize expert-level cardiac diagnostics, dramatically improving early detection and treatment options for children worldwide.

source: https://medicalxpress.com/news/2025-09-ai-tools-critical-gap-pediatric.html