ReviewPediatric radiology2021
Artificial intelligence in child abuse imaging.
Review in Pediatric radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Radiology Staff Experiences With Integrating Artificial Intelligence Into Radiology Practice in a Swedish Hospital: Qualitative Case Study.JMIR formative research · 2025Article
- Fostering Tomorrow: Uniting Artificial Intelligence and Social Pediatrics for Comprehensive Child Well-being.Turkish archives of pediatrics · 2024Article
- Development, contributions, and future directions of a multicenter child abuse research network.Current problems in pediatric and adolescent health care · 2024Article
- Development of a Deep Learning Model for Retinal Hemorrhage Detection on Head Computed Tomography in Young Children.JAMA network open · 2023Article
- European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital age.Pediatric radiology · 2023Article
- Artificial intelligence for radiological paediatric fracture assessment: a systematic review.Insights into imaging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There have been rapid advances in artificial intelligence (AI) technology in recent years, and the field of diagnostic imaging is no exception. Just as digital technology revolutionized how radiology is practiced, so these new technologies also appear poised to bring sweeping change. As AI tools make the transition from the theoretical to the everyday, important decisions need to be made about how they will be applied and what their role will be in the practice of radiology. Pediatric radiology presents distinct challenges and opportunities for the application of these tools, and in this article we discuss some of these, specifically as they relate to the prediction, identification and investigation of child abuse.
Indexed as
Identifiers
33904953What 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.