ReviewPediatric radiology2026
A modern radiologist's guide to artificial intelligence.
Review in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Current State and Future Directions of Diagnostic Imaging and Interpretation in Pediatric Radiology.Diagnostics (Basel, Switzerland) · 2026Article
- Current Trends and Future Prospects of Radiomics and Machine Learning (ML) Models in Spinal Tumors-A Narrative Review.Journal of imaging · 2026Review
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
Artificial intelligence (AI) has the potential to disrupt many fields, and radiology is no exception. The applications of AI in this field go beyond automated diagnosis since they can be used in any stage of the radiological pipeline, from patient referral to image interpretation and recommended course of action. However, it is important to distinguish between clinical usefulness and overpromises. This distinction is especially important for pediatrics, which presents additional challenges like the ethical considerations of working with children, the smaller dataset available for training, and a general lack of explicit labeling that indicates if a tool is suitable for pediatric populations. Here, we give pediatric radiologists a non-technical overview of AI and its subfields, and the potential benefits that it brings to radiology, so they are better equipped to critically evaluate AI and its clinical value. Far from replacing radiologists, AI should be viewed as a companion tool aimed at reducing inefficiencies, enhancing accuracy, and improving patient-centered care.
Indexed as
Identifiers
41686225What 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.