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Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions.
John Joseph Muringathuparambil1, Shamiek Maharaj1, Bradley Max Segal1
1Faculty of Health Sciences, University of the Witwatersrand, 7 York Road, Parktown, Johannesburg, 2193, South Africa.
Pediatric Radiology
|June 3, 2026
Summary
Artificial intelligence (AI) in paediatric imaging requires child-specific design, not adaptation of adult systems. Future tools must prioritize safety, equity, and tailored deployment in diverse healthcare settings, especially in low- and middle-income countries.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Healthcare
- Paediatric Radiology
Background:
- Paediatric chest imaging is crucial for diagnosing childhood diseases, particularly pneumonia in low- and middle-income countries (LMICs).
- Radiology expertise is scarce in LMICs, creating a need for AI to improve access and standardize care.
- Current AI systems are often trained on adult data, raising safety and equity concerns for paediatric use.
Purpose of the Study:
- To review AI applications in paediatric chest radiography, lung ultrasound, CT, and MRI.
- To focus on AI integration within LMIC healthcare workflows.
- To identify requirements for safe, paediatric-specific AI deployment.
Main Methods:
- Narrative and pictorial review of published literature.
- Real-world evaluation of a vision-language model (MedGemma) on adult and paediatric chest radiographs.
- Illustration with clinical cases to demonstrate AI performance and failure modes.
Main Results:
- AI demonstrates potential in quality assurance, ultrasound guidance, and multilingual reporting.
- Real-world evaluations revealed critical failures (e.g., pericardial effusion, tuberculosis detection) without clinical context.
- Key challenges include infrastructure, data scarcity, and the need for age-aware, explainable AI models.
Conclusions:
- AI tools for children must be designed specifically for paediatric needs and environments.
- Prioritize federated learning, multimodal integration, and robust validation across age groups.
- Emphasize multilingual communication and caregiver engagement for effective AI deployment.
