Evidence map›Paper›PMID 36204536›Full record

ArticleRadiology. Artificial intelligence2022

Code and Data Sharing Practices in the Radiology Artificial Intelligence Literature: A Meta-Research Study.

Kesavan Venkatesh, Samantha M Santomartino, Jeremias Sulam, Paul H Yi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Review
  6. Review
  7. Article
  8. Review
  9. Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024
    Article
  10. Observational
  11. Checklist for Reproducibility of Deep Learning in Medical Imaging.Journal of imaging informatics in medicine · 2024
    Article
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. 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

4 authors.

Kesavan VenkateshDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Md (K.V., J.S.); and University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, First Floor, Room 1172, Baltimore, MD 21201 (K.V., S.M.S., P.H.Y.).ORCID https://orcid.org/0000-0001-8153-0115
Samantha M SantomartinoDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Md (K.V., J.S.); and University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, First Floor, Room 1172, Baltimore, MD 21201 (K.V., S.M.S., P.H.Y.).ORCID 0000-0001-5229-2576
Jeremias SulamDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Md (K.V., J.S.); and University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, First Floor, Room 1172, Baltimore, MD 21201 (K.V., S.M.S., P.H.Y.).ORCID 0000-0003-0946-1957
Paul H YiDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Md (K.V., J.S.); and University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, First Floor, Room 1172, Baltimore, MD 21201 (K.V., S.M.S., P.H.Y.).ORCID 0000-0001-9433-8093

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To evaluate code and data sharing practices in original artificial intelligence (AI) scientific manuscripts published in the Radiological Society of North America (RSNA) journals suite from 2017 through 2021. Materials and Methods: A retrospective meta-research study was conducted of articles published in the RSNA journals suite from January 1, 2017, through December 31, 2021. A total of 218 articles were included and evaluated for code sharing practices, reproducibility of shared code, and data sharing practices. Categorical comparisons were conducted using Fisher exact tests with respect to year and journal of publication, author affiliation(s), and type of algorithm used. Results: Of the 218 included articles, 73 (34%) shared code, with 24 (33% of code sharing articles and 11% of all articles) sharing reproducible code. Conclusion: Original AI scientific articles in the RSNA journals suite had low rates of code and data sharing, emphasizing the need for open-source code and data to achieve transparent and reproducible science.

Indexed as

AI in EducationMachine LearningMeta-Analysis

Identifiers

PMID36204536
PMCPMC9530751

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.