Evidence map›Paper›PMID 38375627›Full record

ArticleDiagnostic and interventional radiology (Ankara, Turkey)2024

Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals?

Burak Koçak, Ali Keleş, Fadime Köse

Abstract read
In one paragraph

Article in Diagnostic and interventional radiology (Ankara, Turkey), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Reporting checklists: from a tool after publication to a tool before submission.Diagnostic and interventional radiology (Ankara, Turkey) · 2026
    Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. 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

3 authors.

Burak KoçakUniversity of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, TürkiyeORCID 0000-0002-7307-396X
Ali KeleşUniversity of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, TürkiyeORCID 0000-0003-2474-9983
Fadime KöseUniversity of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, TürkiyeORCID 0000-0003-2445-4974

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions.

methodsThe primary source of journal information and associated citation data used was the Journal Citation Reports (June 2023 release for 2022 citation data; Clarivate Analytics, UK). The first- and second-quartile journals indexed in the Science Citation Index Expanded and the Emerging Sources Citation Index were included. The author and reviewer instructions were evaluated by two independent readers, followed by an additional reader for consensus, with the assistance of automatic annotation. Encouragement and submission requirements were systematically analyzed. The reporting guidelines were grouped as AI-specific, related to modeling, and unrelated to modeling.

resultsOut of 102 journals, 98 were included in this study, and all of them had author instructions. Only five journals (5%) encouraged the authors to follow AI-specific reporting guidelines. Among these, three required a filled-out checklist. Reviewer instructions were found in 16 journals (16%), among which one journal (6%) encouraged the reviewers to follow AI-specific reporting guidelines without submission requirements. The proportions of author and reviewer encouragement for AI-specific reporting guidelines were statistically significantly lower compared with those for other types of guidelines (

conclusionThe findings indicate that AI-specific guidelines are not commonly encouraged and mandated (i.e., requiring a filled-out checklist) by these journals, compared with guidelines related to modeling and unrelated to modeling, leaving vast space for improvement. This meta-research study hopes to contribute to the awareness of the imaging community for AI reporting guidelines and ignite large-scale group efforts by all stakeholders, making AI research less wasteful. CLINICAL SIGNIFICANCE: This meta-research highlights the need for improved encouragement of AI-specific guidelines in radiology, nuclear medicine, and medical imaging journals. This can potentially foster greater awareness among the AI community and motivate various stakeholders to collaborate to promote more efficient and responsible AI research reporting practices.

Indexed as

Artificial IntelligenceNuclear MedicinePeriodicals as TopicRadiologyAuthorshipDiagnostic ImagingGuidelines as TopicHumansArtificial intelligencechecklistguidelinemachine learningreporting

Identifiers

PMID38375627
PMCPMC11590734

What OpenQuestion holds

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LicenceCC BY-NC
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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.