ReviewAJR. American journal of roentgenology2024
Applications of Artificial Intelligence for Pediatric Cancer Imaging.
Review in AJR. American journal of roentgenology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in echocardiography from 2009 to 2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Artificial Intelligence in Pediatric Imaging: A Primer for Pediatric Clinicians.Indian journal of pediatrics · 2026Review
- Multimodality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma.International journal of radiation oncology, biology, physics · 2026Article
- Neurosurgical Application of Artificial Intelligence in Pediatric Neuro-Oncology.Journal of Korean Neurosurgical Society · 2026Article
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- Bibliometric Analysis of Artificial Intelligence in Pediatric Radiology and Medical Imaging: A Focus on Deep Learning Applications.Bioengineering (Basel, Switzerland) · 2026Review
- Innovations in pediatric imaging: a scoping review of the past decade with case illustrations.World journal of pediatrics : WJP · 2026Article
- Application of AIIR algorithm for quality improvement and noise reduction in pediatric abdominal contrast-enhanced CT.Frontiers in radiology · 2026Article
- Integrative precision oncology in neuroblastoma: multi-omics biomarkers, molecular targets, and immunotherapeutic strategies.Frontiers in oncology · 2026Review
- CNN-based detection of pediatric lymphoma on whole body [American journal of nuclear medicine and molecular imaging · 2026Article
- Artificial intelligence and pediatric imaging data: ethical strategies for learning and collaboration.Pediatric radiology · 2025Review
- Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics.Health care analysis : HCA : journal of health philosophy and policy · 2025Article
- Artificial intelligence in pediatric otolaryngology: A state-of-the-art review of opportunities and pitfalls.International journal of pediatric otorhinolaryngology · 2025Review
- Review
- Artificial intelligence in pediatric Wilms tumor imaging: diagnostic performance and the need for clinical oversight.Jornal brasileiro de nefrologiaArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Artificial intelligence (AI) is transforming the medical imaging of adult patients. However, its utilization in pediatric oncology imaging remains constrained, in part due to the inherent scarcity of data associated with childhood cancers. Pediatric cancers are rare, and imaging technologies are evolving rapidly, leading to insufficient data of a particular type to effectively train these algorithms. The small market size of pediatric patients compared with adult patients could also contribute to this challenge, as market size is a driver of commercialization. This review provides an overview of the current state of AI applications for pediatric cancer imaging, including applications for medical image acquisition, processing, reconstruction, segmentation, diagnosis, staging, and treatment response monitoring. Although current developments are promising, impediments due to the diverse anatomies of growing children and nonstandardized imaging protocols have led to limited clinical translation thus far. Opportunities include leveraging reconstruction algorithms to achieve accelerated low-dose imaging and automating the generation of metric-based staging and treatment monitoring scores. Transfer learning of adult-based AI models to pediatric cancers, multiinstitutional data sharing, and ethical data privacy practices for pediatric patients with rare cancers will be keys to unlocking the full potential of AI for clinical translation and improving outcomes for these young patients.
Indexed as
Identifiers
What Socratic 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 Socratic graph.