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Artificial Intelligence in Pediatric Imaging: A Primer for Pediatric Clinicians
Amit Gupta1, Anjali Agrawal2, Marla Sammer3
1Department of Pediatric and Onco-Radiology, AIIMS, New Delhi, 110029, India.
Indian Journal of Pediatrics
|June 19, 2026
Summary
Artificial intelligence (AI) is advancing pediatric radiology, offering benefits in areas like bone age and neuroimaging. However, challenges in data, validation, and integration hinder widespread clinical adoption for children.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Artificial intelligence (AI) is increasingly adopted in general radiology due to rising imaging demand and workforce shortages.
- Clinical translation of AI in pediatric imaging lags behind general radiology, with limited approved tools for specific pediatric indications.
Purpose of the Study:
- To review the current state and future potential of AI in pediatric radiology.
- To identify applications, benefits, and barriers to AI adoption in pediatric imaging.
Main Methods:
- Review of current AI applications in pediatric imaging, including chest, neuro, cardiovascular, and oncology.
- Discussion of challenges such as data scarcity, validation, workflow integration, and ethical considerations.
Main Results:
- AI shows promise in automated bone age assessment, pneumonia detection, neuroimaging triage, and cardiovascular analysis in children.
- Significant barriers include limited pediatric datasets, insufficient prospective validation, workflow integration issues, and unique ethical/legal concerns for pediatric patients.
Conclusions:
- Collaborative efforts between pediatricians, surgeons, radiologists, and data scientists are crucial for defining use cases and validating AI tools.
- Future progress requires data sharing, robust validation, clinician engagement, and child-centered guidelines for safe and equitable AI adoption in pediatric radiology.
