Clinical Report: The Role of Artificial Intelligence in Pediatric Congenital Heart Disease
Overview
This bibliometric review analyzes the growth and impact of artificial intelligence (AI) in diagnosing and managing pediatric congenital heart disease (CHD) over 25 years. It highlights the need for further research and implementation of AI tools in clinical settings, particularly in low-resource environments.
Background
Congenital heart disease (CHD) significantly contributes to childhood morbidity and mortality, with over 4.18 million affected children globally as of 2021. The integration of AI in pediatric cardiology has the potential to enhance diagnostic accuracy and patient management. However, challenges such as data scarcity and regulatory hurdles hinder its widespread adoption, particularly in low- and middle-income countries.
Data Highlights
This study employs bibliometric methods to quantify AI research in pediatric CHD from 2000 to 2025, identifying key contributors and trends in the field.
Key Findings
AI applications in pediatric CHD have been explored for over 20 years, with a growing body of literature.
There is a notable gap between academic research and practical implementation in low-resource settings.
Key areas of focus include diagnosis, prognosis prediction, and personalized patient care.
Research trends indicate a shift from pathophysiological studies to innovations driven by deep learning.
Collaboration networks and research clusters have been mapped to understand the intellectual structure of the field.
Clinical Implications
Clinicians and researchers should be aware of the evolving landscape of AI in pediatric CHD, emphasizing the importance of addressing implementation challenges. Policymakers are encouraged to support research that aligns with clinical needs, especially in resource-limited settings.
Conclusion
The study provides a comprehensive overview of the role of AI in pediatric CHD, highlighting both the progress made and the challenges that remain in translating research into clinical practice.
In a multicenter registry study, genetic diagnoses were associated with substantially lower cognitive, language, and motor scores; while birth weight, surgical timing, hospitalization burden, and caregiver education were also associated with outcomes.
