Evidence map›Paper›PMID 41579167›Full record

ArticlePediatric radiology2026

The current state of artificial intelligence research in pediatric radiology and recommendations for the future: a scoping review.

Rakhshan Kamran, Elysa Widjaja, Alex Sy, Jessica Bosso, Lomesh Choudhary, Alexandra Lawrynuik, Yu Xuan Jin, Cynthia Chan, Nasana Vaidya, Sarah Larrigan and 19 more

Abstract readScoping Review
PubMed Publisher
In one paragraph

Article in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

29 authors.

Rakhshan KamranUniversity of Oxford, Oxford, United Kingdom. rakhshan.kamran@mail.utoronto.ca.
Elysa WidjajaDepartment of Radiology, Feinberg School of Medicine, Northwestern University, Evanston, United States.
Alex SyFaculty of Medicine, University of Ottawa, Ottawa, Canada.
Jessica BossoSchool of Medicine, McMaster University, Hamilton, Canada.
Lomesh ChoudharySchool of Medicine, McMaster University, Hamilton, Canada.
Alexandra LawrynuikSchool of Medicine, McMaster University, Hamilton, Canada.
Yu Xuan JinFaculty of Medicine, University of British Columbia, Vancouver, Canada.
Cynthia ChanDepartment of Family and Community Medicine, University of Toronto, Toronto, Canada.
Nasana VaidyaDepartment of Immunology, University of Toronto, Toronto, Canada.
Sarah LarriganOttawa Hospital Research Institute (OHRI), Ottawa Hospital, Ottawa, Canada.
Liam JackmanTemerty Faculty of Medicine, University of Toronto, Toronto, Canada.
Yujin SukDepartment of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Canada.
Laura LarriganFaculty of Medicine, University of Ottawa, Ottawa, Canada.
Ann LeeDepartment of Pediatrics, University of Ottawa, Ottawa, Canada.
Geetika KhannaDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, United States.
Andrew TroutDepartment of Radiology, University of Cincinnati, Cincinnati, United States.
Marla SammerDepartment of Radiology, Texas Children's Hospital, Houston, United States.
Randolph OttoDepartment of Radiology, University of Washington, Seattle, United States.
Michael GeeDepartment of Radiology, Harvard University, Cambridge, United States.
Cara MorinDepartment of Radiology, University of Cincinnati, Cincinnati, United States.
Mai-Lan HoDepartment of Radiology, Nationwide Children's Hospital, Columbus, United States.
Meghna GaddamDepartment of Radiology, Feinberg School of Medicine, Northwestern University, Evanston, United States.
Hansel OteroDepartment of Radiology, Children's Hospital of Philadelphia, Philadelphia, United States.
Sara Reis TeixeiraDepartment of Radiology, Children's Hospital of Philadelphia, Philadelphia, United States.
M Alejandra BedoyaThe Hospital for Sick Children, Department of Diagnostic Imaging, 555 University Avenue, Toronto, ON, M5G1X8, Canada.
Andy TsaiDepartment of Radiology, Harvard University, Cambridge, United States.
Savvas AndronikouDepartment of Radiology, University of Pennsylvania, Philadelphia, United States.
Sherwin ChanDepartment of Radiology, University of Missouri-Kansas City, Kansas City, United States.
Andrea S DoriaDepartment of Medical Imaging, University of Toronto, Toronto, Canada. andrea.doria@sickkids.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMost artificial intelligence (AI) research in radiology has focused on adults. Understanding macro-level trends in pediatric radiology AI can help guide, streamline, and bolster future research.

objectiveTo detail the current landscape of published AI research in pediatric radiology, filling a key research gap, as most radiology AI research has focused on adults. MATERIALS AND

methodsWe conducted a scoping review, with a comprehensive literature search of Medline, Embase, Web of Science, and Cochrane Library from 2005 to 2024. Literature included for review were (1) original articles, (2) investigations that focused on pediatric populations (<18 years of age), and (3) articles with direct applications to clinical radiology and AI. We extracted each article's study information, clinical application of focus, imaging modality, and the use of AI. We used descriptive frequencies to analyze summary statistics, and Chi-square testing to determine differences between categories.

resultsIn total, we found 4,376 articles and included 789 articles in the review. The top three countries most active in scholarship related to AI in pediatric radiology were China (220, 27.9%), the USA (200, 25.4%), and Canada (51, 6.5%) (P<0.001). The most common imaging modalities were radiography (298, 37.8%), MRI (260, 33.0%), and ultrasonography (114, 14.4%) (P<0.001). The most common subspecialties represented were musculoskeletal (260, 33.0%), neurological (227, 28.8%), and chest imaging (130, 16.5%) (P<0.001). The top two image analysis tasks discussed were image interpretation/diagnosis (719, 91.1%), and artifact and motion reduction/enhancing image quality (44, 5.6%) (P<0.001).

conclusionMost pediatric radiology AI research originated from China and the USA, and focused on image interpretation/diagnosis. Thematic imbalances, particularly underrepresentation in research on communication, education, policy, and stakeholder perspectives, offer a guide for pediatric radiology AI development. There is a need for improved global collaboration and improved patient representativeness in datasets for pediatric radiology AI research to reduce bias with AI algorithms. The results from this scoping review offer a practical roadmap to inform future research and funding priorities in pediatric radiology AI.

Indexed as

Artificial IntelligenceBiomedical ResearchPediatricsRadiologyChildForecastingHumansArtificial intelligenceChildrenCTImaging interpretation/diagnosisMRINuclear medicinePediatric radiologyRadiographyResearchScoping reviewUltrasound

Identifiers

PMID41579167

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.