Evidence map›Paper›PMID 41288670›Full record

GuidelinePediatric radiology2026

AI implementation in pediatric radiology for patient safety: a multi-society statement from the ACR, ESPR, SPR, SLARP, AOSPR, SPIN.

Susan C Shelmerdine, Jaishree Naidoo, Brendan S Kelly, Lene Bjerke Laborie, Seema Toso, Tugba Akinci D'Antonoli, Owen J Arthurs, Steven L Blumer, Pierluigi Ciet, Maria Beatrice Damasio and 20 more

Erratum issuedAbstract readPractice GuidelineReview
PubMed Publisher
In one paragraph

Guideline in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  13. Reply to Srikanth M.Pediatric radiology · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

30 authors.

Susan C ShelmerdineGreat Ormond Street Hospital, London, WC1N 3JH, UK. susan.shelmerdine@gosh.nhs.uk.
Jaishree NaidooEnvisionit Deep AI Ltd, Cobham, UK.
Brendan S KellyGreat Ormond Street Hospital, London, WC1N 3JH, UK.
Lene Bjerke LaborieDept of Clinical Medicine, University of Bergen, Bergen, Norway.
Seema TosoGeneva Children's Hospital, Geneva, Switzerland.
Tugba Akinci D'AntonoliDepartment of Diagnostic and Interventional Neuroradiology, University Hospital of Basel, Basel, Switzerland.
Owen J ArthursGreat Ormond Street Hospital, London, WC1N 3JH, UK.
Steven L BlumerUniversity of Pittsburgh Medical Center, Pittsburgh, USA.
Pierluigi CietErasmus MC - Sophia Children's Hospital, Rotterdam, Netherlands.
Maria Beatrice DamasioIstituto Giannina Gaslini, Genoa, Italy.
Andrea S DoriaHospital for Sick Children, Toronto, Canada.
Saira HaqueKing's College Hospital, London, UK.
Mai-Lan HoUniversity of Missouri, Columbia, USA.
Theirry Agm HuismanTexas Children's Hospital, Houston, USA.
Aparna JoshiUniversity of Michigan-Ann Arbor, Ann Arbor, USA.
Jeevesh KapurNational University of Singapore, Singapore, Singapore.
Kshitij MankadGreat Ormond Street Hospital, London, WC1N 3JH, UK.
Amaka C OffiahUniversity of Sheffield, Sheffield, UK.
Hansel OteroChildren's Hospital of Philadelphia, Philadelphia, USA.
Erika PaceRoyal Marsden NHS Foundation Trust, London, UK.
Tom SempleRoyal Brompton & Harefield NHS Foundation Trust, London, UK.
Kushaljit Singh SodhiPost Graduate Institute of Medical Education and Research, Chandigarh, India.
Sebastian TschaunerMedical University of Graz, Graz, Austria.
Carlos F Ugas-CharcapeInstituto Nacional de Salud del Niño, Lima, Peru.
Dhananjaya K VamyanmaneIndira Gandhi Institute of Child Health, Bengaluru, India.
Rick R van RijnDepartment of Radiology and Nuclear Medicine, Emma Children's Hospital, University of Amsterdam, Amsterdam, Netherlands.
Diana Veiga-CanutoHospital for Sick Children, Toronto, Canada.
Matthias W WagnerHospital for Sick Children, Toronto, Canada.
Evan J ZuckerDepartment of Radiology, Stanford University, Stanford, USA.
Marla SammerTexas Children's Hospital, Houston, USA.

Funding

National Institute for Health and Care Research 301322
6 · The paper itself

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.

Indexed as

Artificial IntelligencePatient SafetyPediatricsRadiologyChildHumansSocieties, MedicalUnited StatesArtificial intelligenceChildrenImplementationRadiologySafety

Identifiers

PMID41288670

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.