SynthesisEuropean radiology2024
Self-reporting with checklists in artificial intelligence research on medical imaging: a systematic review based on citations of CLAIM.
Synthesis in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled 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.
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
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Self-reporting with checklists in artificial intelligence research on medical imaging: a systematic review based on citations of CLAIM.European radiology · 2024Pooled it
- The endorsement of general and artificial intelligence reporting guidelines in radiological journals: a meta-research study.BMC medical research methodology · 2023Pooled it
- Echo Chambers: Bias and Representation in Cardiac Imaging Datasets for Artificial Intelligence.Current cardiology reports · 2026Review
- Reporting checklists: from a tool after publication to a tool before submission.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Assessing the quality of reporting in artificial intelligence/machine learning research for cardiac amyloidosis.JAMIA open · 2025Article
- Adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM): an umbrella review with a comprehensive two-level analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Explanation and Elaboration with Examples for METRICS (METRICS-E3): an initiative from the EuSoMII Radiomics Auditing Group.Insights into imaging · 2025Article
- Reproducibility of methodological radiomics score (METRICS): an intra- and inter-rater reliability study endorsed by EuSoMII.European radiology · 2025Article
- Artificial Intelligence Approaches for Geographic Atrophy Segmentation: A Systematic Review and Meta-Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- Reporting checklists as compulsory supplements to artificial intelligence manuscript submissions.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals?Diagnostic and interventional radiology (Ankara, Turkey) · 2024Article
- Self-reported checklists and quality scoring tools in radiomics: a meta-research.European radiology · 2024Article
- Letter to the Editor: "Obsolescence of nomograms in radiomics research".European radiology · 2024Article
- Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update.Radiology. Artificial intelligence · 2024Article
- Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative.European radiology experimental · 2024Article
- METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII.Insights into imaging · 2024Article
- Advancing radiomics research translation through a public database.European radiology · 2024Article
- Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study.Insights into imaging · 2023Article
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
objectiveTo evaluate the usage of a well-known and widely adopted checklist, Checklist for Artificial Intelligence in Medical imaging (CLAIM), for self-reporting through a systematic analysis of its citations.
methodsGoogle Scholar, Web of Science, and Scopus were used to search for citations (date, 29 April 2023). CLAIM's use for self-reporting with proof (i.e., filled-out checklist) and other potential use cases were systematically assessed in research papers. Eligible papers were evaluated independently by two readers, with the help of automatic annotation. Item-by-item confirmation analysis on papers with checklist proof was subsequently performed.
resultsA total of 391 unique citations were identified from three databases. Of the 118 papers included in this study, 12 (10%) provided a proof of self-reported CLAIM checklist. More than half (70; 59%) only mentioned some sort of adherence to CLAIM without providing any proof in the form of a checklist. Approximately one-third (36; 31%) cited the CLAIM for reasons unrelated to their reporting or methodological adherence. Overall, the claims on 57 to 93% of the items per publication were confirmed in the item-by-item analysis, with a mean and standard deviation of 81% and 10%, respectively.
conclusionOnly a small proportion of the publications used CLAIM as checklist and supplied filled-out documentation; however, the self-reported checklists may contain errors and should be approached cautiously. We hope that this systematic citation analysis would motivate artificial intelligence community about the importance of proper self-reporting, and encourage researchers, journals, editors, and reviewers to take action to ensure the proper usage of checklists. CLINICAL RELEVANCE STATEMENT: Only a small percentage of the publications used CLAIM for self-reporting with proof (i.e., filled-out checklist). However, the filled-out checklist proofs may contain errors, e.g., false claims of adherence, and should be approached cautiously. These may indicate inappropriate usage of checklists and necessitate further action by authorities. KEY POINTS: • Of 118 eligible papers, only 12 (10%) followed the CLAIM checklist for self-reporting with proof (i.e., filled-out checklist). More than half (70; 59%) only mentioned some kind of adherence without providing any proof. • Overall, claims on 57 to 93% of the items were valid in item-by-item confirmation analysis, with a mean and standard deviation of 81% and 10%, respectively. • Even with the checklist proof, the items declared may contain errors and should be approached cautiously.
Indexed as
Identifiers
37740080What 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.