Evidence map›Paper›PMID 34046708›Full record

ReviewPediatric radiology2022

The current and future roles of artificial intelligence in pediatric radiology.

Jeffrey P Otjen, Michael M Moore, Erin K Romberg, Francisco A Perez, Ramesh S Iyer

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pediatric radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–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

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
  6. Review
  7. Review
  8. Review
  9. Article
  10. Review
  11. Review
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article
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

5 authors.

Jeffrey P OtjenDepartment of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, 4800 Sand Point Way NE, MA.7.220, Seattle, WA, 98105, USA.
Michael M MooreDepartment of Radiology, Penn State Children's Hospital, Penn State Health System, Hershey, PA, USA.
Erin K RombergDepartment of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, 4800 Sand Point Way NE, MA.7.220, Seattle, WA, 98105, USA.
Francisco A PerezDepartment of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, 4800 Sand Point Way NE, MA.7.220, Seattle, WA, 98105, USA.
Ramesh S IyerDepartment of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, 4800 Sand Point Way NE, MA.7.220, Seattle, WA, 98105, USA. riyer@uw.edu.ORCID 0000-0001-7648-3596

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is a broad and complicated concept that has begun to affect many areas of medicine, perhaps none so much as radiology. While pediatric radiology has been less affected than other radiology subspecialties, there are some well-developed and some nascent applications within the field. This review focuses on the use of AI within pediatric radiology for image interpretation, with descriptive summaries of the literature to date. We highlight common features that enable successful application of the technology, along with some of the limitations that can inhibit the development of this field. We present some ideas for further research in this area and challenges that must be overcome, with an understanding that technology often advances in unpredictable ways.

Indexed as

Artificial IntelligenceRadiologyChildForecastingHumansApplicationsArtificial intelligenceChildrenConvolutional neural networkPediatric radiologyResearchTechnology

Identifiers

PMID34046708

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.