TY - JOUR
T1 - Artificial Intelligence Implementation in Pediatric Radiology for Patient Safety
T2 - A Multisociety Statement From the ACR, ESPR, SPR, SLARP, AOSPR, SPIN
AU - Shelmerdine, Susan C.
AU - Naidoo, Jaishree
AU - Kelly, Brendan S.
AU - Laborie, Lene Bjerke
AU - Toso, Seema
AU - D'Antonoli, Tugba Akinci
AU - Arthurs, Owen J.
AU - Blumer, Steven L.
AU - Ciet, Pierluigi
AU - Damasio, Maria Beatrice
AU - Doria, Andrea S.
AU - Haque, Saira
AU - Ho, Mai Lan
AU - Huisman, Thierry A.G.M.
AU - Joshi, Aparna
AU - Kapur, Jeevesh
AU - Mankad, Kshitij
AU - Offiah, Amaka C.
AU - Otero, Hansel J.
AU - Pace, Erika
AU - Semple, Tom
AU - Sodhi, Kushaljit Singh
AU - Tschauner, Sebastian
AU - Ugas-Charcape, Carlos F.
AU - Vamyanmane, Dhananjaya K.
AU - van Rijn, Rick R.
AU - Veiga-Canuto, Diana
AU - Wagner, Matthias W.
AU - Zucker, Evan J.
AU - Sammer, Marla
N1 - Publisher Copyright:
© 2025 The Author(s)
PY - 2026/1
Y1 - 2026/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105023200197
U2 - 10.1016/j.jacr.2025.08.019
DO - 10.1016/j.jacr.2025.08.019
M3 - Review article
C2 - 41288534
AN - SCOPUS:105023200197
SN - 1546-1440
VL - 23
SP - 89
EP - 101
JO - Journal of the American College of Radiology
JF - Journal of the American College of Radiology
IS - 1
ER -