AI implementation in pediatric radiology for patient safety: a multi-society statement from the ACR, ESPR, SPR, SLARP, AOSPR, SPIN.
Authors
Affiliations (39)
Affiliations (39)
- Great Ormond Street Hospital, London, WC1N 3JH, UK. [email protected].
- Great Ormond Street Hospital Biomedical Research Centre, London, UK. [email protected].
- Envisionit Deep AI Ltd, Cobham, UK.
- Dr J Naidoo Inc., Johannesburg, South Africa.
- Great Ormond Street Hospital, London, WC1N 3JH, UK.
- Dept of Clinical Medicine, University of Bergen, Bergen, Norway.
- Mohn Medical Imaging and Visualization Centre, Haukeland University Hospital, Bergen, Norway.
- Geneva Children's Hospital, Geneva, Switzerland.
- University Hospital of Geneva, Geneva, Switzerland.
- Department of Diagnostic and Interventional Neuroradiology, University Hospital of Basel, Basel, Switzerland.
- Department of Radiology, University Children's Hospital Basel, Basel, Switzerland.
- Great Ormond Street Hospital Biomedical Research Centre, London, UK.
- University of Pittsburgh Medical Center, Pittsburgh, USA.
- Erasmus MC - Sophia Children's Hospital, Rotterdam, Netherlands.
- University of Cagliari, Cagliari, Italy.
- Istituto Giannina Gaslini, Genoa, Italy.
- Hospital for Sick Children, Toronto, Canada.
- University of Toronto, Toronto, Canada.
- King's College Hospital, London, UK.
- University of Missouri, Columbia, USA.
- Texas Children's Hospital, Houston, USA.
- University of Michigan-Ann Arbor, Ann Arbor, USA.
- National University of Singapore, Singapore, Singapore.
- University of Sheffield, Sheffield, UK.
- Sheffield Children's NHS Foundation Trust, Sheffield, UK.
- Children's Hospital of Philadelphia, Philadelphia, USA.
- University of Pennsylvania, Philadelphia, USA.
- Royal Marsden NHS Foundation Trust, London, UK.
- Institute of Cancer Research, London, UK.
- Royal Brompton & Harefield NHS Foundation Trust, London, UK.
- Guy's and St Thomas' NHS Foundation Trust, London, UK.
- Post Graduate Institute of Medical Education and Research, Chandigarh, India.
- Medical University of Graz, Graz, Austria.
- Instituto Nacional de Salud del Niño, Lima, Peru.
- Rainbow Babies & Children's Hospital, Cleveland, USA.
- Indira Gandhi Institute of Child Health, Bengaluru, India.
- Department of Radiology and Nuclear Medicine, Emma Children's Hospital, University of Amsterdam, Amsterdam, Netherlands.
- Department of Radiology, Stanford University, Stanford, USA.
- Department of Radiology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA.
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 multi-society position statement systematically addresses four critical pillars of AI adoption: (1) regulation and purchasing, (2) implementation and integration, (3) interpretation and post-market 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 post-market 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 healthcare professionals which 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.