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Artificial Intelligence Implementation in Pediatric Radiology for Patient Safety: A Multisociety Statement From the
Susan C Shelmerdine1, Jaishree Naidoo2, Brendan S Kelly3
1Department of Clinical Radiology, Great Ormond Street Hospital for Children, London, UK; UCL Great Ormond Street Institute of Child Health, Great Ormond Street Hospital for Children, London, UK; National Institute for Health and Care Research Great Ormond Street Hospital Biomedical Research Centre, Bloomsbury, London, UK.
Insights
Artificial intelligence (AI) offers great potential in pediatric radiology. This position statement outlines a child-centered framework for safe AI integration, emphasizing tailored regulation, implementation, and education for children's unique needs.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Current AI in radiology primarily serves adults, neglecting children's distinct physiological and developmental needs.
- Safe and effective AI integration in pediatric radiology requires specialized approaches.
- A multisociety position statement addresses critical pillars for AI adoption in pediatrics.
Purpose of the Study:
- To propose a child-centered framework for the adoption of artificial intelligence in pediatric radiology.
- To outline specific recommendations for regulation, implementation, interpretation, and education of AI tools for pediatric use.
- To ensure the safety, accuracy, and well-being of children when using AI in medical imaging.
Main Methods:
- Systematic review and position statement development by multiple societies.
- Proposal of pediatric-specific safety ratings, diverse datasets, transparency metrics, and explainability.
- Recommendation of a phased implementation strategy with pilot testing and continuous postmarket surveillance.
- Emphasis on foundational AI literacy and specialized training for healthcare professionals.
Main Results:
- Advocacy for pediatric-specific safety ratings and diverse datasets to mitigate bias.
- Recommendations for phased implementation, stakeholder engagement, and robust postmarket surveillance.
- Highlighting the necessity of AI literacy and specialized training for healthcare professionals.
- Stress on public and patient engagement for AI acceptance in pediatric radiology.
Conclusions:
- A child-centered framework is essential for integrating AI in pediatric radiology.
- Prioritizing children's unique needs ensures the safe and effective use of AI in medical imaging.
- Tailored regulation, implementation, and education are crucial for successful AI adoption in pediatric radiology.
Abstract:
Artificial intelligence (AI) has potential to revolutionize radiology, yet current solutions and guidelines are predominantly focused on adult populations, often overlooking the specific requirements of children. This is important because children differ significantly from adults in terms of physiology, developmental stages, and clinical needs, necessitating tailored approaches for the safe and effective integration of AI tools. This multisociety position statement systematically addresses four critical pillars of AI adoption: (1) regulation and purchasing, (2) implementation and integration, (3) interpretation and postmarket surveillance, and (4) education. We propose pediatric-specific safety ratings, inclusion of datasets from diverse pediatric populations, quantifiable transparency metrics, and explainability of models to mitigate biases and ensure AI systems are appropriate for use in children. Risk assessment, dataset diversity, transparency, and cybersecurity are important steps in regulation and purchasing. For successful implementation, a phased strategy is recommended, involving early pilot testing, stakeholder engagement, and comprehensive postmarket surveillance with continuous monitoring of defined performance benchmarks. Clear protocols for managing discrepancies and adverse incident reporting are essential to maintain trust and safety. Moreover, we emphasize the need for foundational AI literacy courses for all health care professionals that include pediatric safety considerations, alongside specialized training for those directly involved in pediatric imaging. Public and patient engagement is crucial to foster understanding and acceptance of AI in pediatric radiology. Ultimately, we advocate for a child-centered framework for AI integration, ensuring that the distinct needs of children are prioritized and that their safety, accuracy, and overall well-being are safeguarded.
