GuidelinePediatric radiology2026
AI implementation in pediatric radiology for patient safety: a multi-society statement from the ACR, ESPR, SPR, SLARP, AOSPR, SPIN.
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.
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.
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.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.Current oncology reports · 2026Pooled it
- Performance of Commercial Deep Learning-Based Radiation Dose Optimization Software in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2026Review
- Perspectives on the future of artificial intelligence in paediatric radiology.Pediatric radiology · 2026Article
- Perspectives on the future of artificial intelligence in paediatric radiology.Pediatric radiology · 2026Article
- The role of artificial intelligence in paediatric abdominal imaging.Pediatric radiology · 2026Review
- Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.Pediatric radiology · 2026Review
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- Artificial Intelligence in Pediatric Imaging: A Primer for Pediatric Clinicians.Indian journal of pediatrics · 2026Review
- Prompting the future: artificial intelligence in pediatric radiology.Pediatric radiology · 2026Article
- Artificial intelligence for pediatric neuroimaging.Pediatric radiology · 2026Review
- Artificial intelligence, equity, and pediatric neurodevelopmental disorders: A scoping review of clinical practice applications.Pediatric investigation · 2026Review
- AI implementation in pediatric radiology for patient safety: a multisociety statement from the ACR, ESPR, SPR, SLARP, AOSPR, SPIN: Reply to Shelmerdine et al.Pediatric radiology · 2026Article
- Reply to Srikanth M.Pediatric radiology · 2026Article
- From guidance to accountability in safe artificial intelligence for children: reply to Shelmerdine et al.Pediatric radiology · 2026Article
- Bibliometric Analysis of Artificial Intelligence in Pediatric Radiology and Medical Imaging: A Focus on Deep Learning Applications.Bioengineering (Basel, Switzerland) · 2026Review
- Data mining in pediatric radiology in the era of artificial intelligence.Pediatric radiology · 2026Review
- Artificial intelligence in pediatric Wilms tumor imaging: diagnostic performance and the need for clinical oversight.Jornal brasileiro de nefrologiaArticle
Corrections and comments
- Erratum issued
Authors and funding
30 authors.
Funding
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
Identifiers
41288670What OpenQuestion holds
Registered trials
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.