The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers
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well the data match the intended use of the model.
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The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Current standards for reporting model performance in ML-based science often fall short in addressing issues caused by leakage.”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [38]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Further, most checklists currently in use are not developed for ML-based science in general but rather for specific scientific or research communities.4,38”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [38]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Specifically, checklists and model cards are one way to provide standard best practices for reporting details about ML models.38,39,40”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [38]
The cited source says what the claim attributes to it.
In the citing paper
“The quality of the selected studies was rigorously assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) alongside the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2).23,24”
From Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging (2024) · cites this paper as [23]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“However, these guidelines do not cover RAG applications in clinical settings, emphasizing the need for a more specific guideline.”
From Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines (2025) · cites this paper as [12]
Experimental — accuracy not yet measured
In the citing paper
“Following guidelines for research documentation and complete reporting increases transparency. 63,64”
From AI pitfalls and what not to do: mitigating bias in AI (2023) · cites this paper as [63]
In the citing paper
“Human-labeled annotations (eg, segmentation masks or bounding boxes) require a clear protocol and measurement of intra- and interannotator reliability to reduce recall, observer, or measurement biases (24).”
From Mitigating Bias in Radiology Machine Learning: 1. Data Handling (2022) · cites this paper as [24]
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Citations
Cites
CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
the CONSORT Group, Schulz, K. F., Altman, D. G., & Moher, D. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-18 · Cited by 13,371
CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
Schulz, K. F., Altman, D. G., Moher, D., & for the CONSORT Group (2010) · BMJ · doi:10.1136/bmj.c332 · Cited by 9,329
Vandenbroucke, J. P., von Elm, E., Altman, D. G., Gøtzsche, P. C., Mulrow, C. D., Pocock, S. J., Poole, C., Schlesselman, J. J., Egger, M., & Initiative, F. T. S. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040297 · Cited by 8,613
Radiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688
Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD Initiative.
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2003) · Annals of Internal Medicine · doi:10.7326/0003-4819-138-1-200301070-00010 · Cited by 3,219
Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., Lijmer, J. G., Moher, D., Rennie, D., & de Vet, H. C. W. (2003) · BMJ · doi:10.1136/bmj.326.7379.41 · Cited by 3,215
A Critical Review of Recurrent Neural Networks for Sequence LearningPreprint
Lipton, Z. C., Berkowitz, J. A., & Elkan, C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00019 · Cited by 3,159
Improving the Quality of Reporting of Randomized Controlled Trials: The CONSORT Statement
Begg, C. (1996) · JAMA · doi:10.1001/jama.1996.03540080059030 · Cited by 3,127
STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration
Cohen, J. F., Korevaar, D. A., Altman, D. G., Bruns, D. E., Gatsonis, C. A., Hooft, L., Irwig, L., Levine, D., Reitsma, J. B., de Vet, H. C. W., et al. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-012799 · Cited by 2,570
STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C., Glasziou, P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2015) · Radiology · doi:10.1148/radiol.2015151516 · Cited by 1,360
Preparing Medical Imaging Data for Machine Learning
Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P. (2020) · Radiology · doi:10.1148/radiol.2020192224 · Cited by 1,020
Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., Shilton, A., Yearwood, J., Dimitrova, N., Ho, T. B., et al. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5870 · Cited by 988
STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2015) · Clinical Chemistry · doi:10.1373/clinchem.2015.246280 · Cited by 984
Park, S. H., & Han, K. (2018) · Radiology · doi:10.1148/radiol.2017171920 · Cited by 894
Sample Size Estimation: How Many Individuals Should Be Studied?
Eng, J. (2003) · Radiology · doi:10.1148/radiol.2272012051 · Cited by 811
Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Huang, S., Brooks, M., Lee, M. J., & Asadi, H. (2018) · American Journal of Roentgenology · doi:10.2214/ajr.18.20224 · Cited by 434
Geis, J. R., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E., Jaremko, J. L., Langer, S. G., Borondy Kitts, A., Birch, J., Shields, W. F., et al. (2019) · Radiology · doi:10.1148/radiol.2019191586 · Cited by 388
Bluemke, D. A., Moy, L., Bredella, M. A., Ertl-Wagner, B. B., Fowler, K. J., Goh, V. J., Halpern, E. F., Hess, C. P., Schiebler, M. L., & Weiss, C. R. (2020) · Radiology · doi:10.1148/radiol.2019192515 · Cited by 343
Self-citationEQUATOR: reporting guidelines for health research
Altman, D. G., Simera, I., Hoey, J., Moher, D., & Schulz, K. (2008) · The Lancet · doi:10.1016/s0140-6736(08)60505-x · Cited by 325
EQUATOR: reporting guidelines for health research.
Altman, D. G., Simera, I., Hoey, J., Moher, D., & Schulz, K. (2008) · PubMed Central · Cited by 224
Bossuyt, P. M., & Reitsma, J. B. (2003) · The Lancet · doi:10.1016/s0140-6736(03)12122-8 · Cited by 147
Improving the value of clinical research through the use of Common Data Elements
Sheehan, J. R., Hirschfeld, S., Foster, E. D., Ghitza, U. E., Goetz, K., Karpinski, J., Lang, L., Moser, R. P., Odenkirchen, J. C., Reeves, D. M., et al. (2016) · Clinical Trials · doi:10.1177/1740774516653238 · Cited by 130
A Systematic Guide to Reviewing a Manuscript
Provenzale, J. M., & Stanley, R. J. (2005) · American Journal of Roentgenology · doi:10.2214/ajr.05.0782 · Cited by 122
Geis, J. R., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E. R., Jaremko, J. L., Langer, S. G., Kitts, A. B., Birch, J. V., Shields, W. F., et al. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.07.028 · Cited by 104
Common Data Elements in Radiology
Rubin, D. L., & Kahn, C. E. (2016) · Radiology · doi:10.1148/radiol.2016161553 · Cited by 97
Self-citationPark, S. H., & Kressel, H. Y. (2018) · Journal of Korean Medical Science · doi:10.3346/jkms.2018.33.e152 · Cited by 70
A Standardised Approach for Preparing Imaging Data for Machine Learning Tasks in Radiology
Harvey, H., & Glocker, B. (2019) · Artificial Intelligence in Medical Imaging · doi:10.1007/978-3-319-94878-2_6 · Cited by 45
Bending the Artificial Intelligence Curve for Radiology: Informatics Tools From ACR and RSNA
Kohli, M. D., Alkasab, T. K., Wang, K., Heilbrun, M. E., Flanders, A. E., Dreyer, K. J., & Kahn, C. E. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.06.009 · Cited by 38
Self-citationSimera, I., & Altman, D. G. (2013) · International Journal of Clinical Practice · doi:10.1111/ijcp.12168 · Cited by 31
Artificial Intelligence, Real Radiology
Kahn, C. E. (2019) · Radiology Artificial Intelligence · doi:10.1148/ryai.2019184001 · Cited by 27
Self-citationEvidence-Based Radiology: A Primer in Reading Scientific Articles
Budovec, J. J., & Kahn, C. E. (2010) · American Journal of Roentgenology · doi:10.2214/ajr.10.4696 · Cited by 24
Self-citationLakhani, P. C., Kim, W. J., & Langlotz, C. P. (2012) · Radiology · doi:10.1148/radiol.12112438 · Cited by 21
Flagged first, then by citations
Research works printed but not linked: 3
- 10.1148/radiol.2261021292 · DOI · Paper page
- Willemink MJ, 2020, Radiology, 192224
- National Institutes of Health, NIH Common Data Elements (CDE) Repository
Cited by
Sorted by citations
Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-078378 · Cited by 4,718
Leakage and the reproducibility crisis in machine-learning-based science
Kapoor, S., & Narayanan, A. (2023) · Patterns · doi:10.1016/j.patter.2023.100804 · Cited by 1,387
“Further, most checklists currently in use are not developed for ML-based science in general but rather for specific scientific or research communities.(4,38) As a result, best practices for model reporting in ML-based science are underspecified.”
BackgroundRoberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00307-0 · Cited by 1,044
Roberts, M. T., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Avilés-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2020) · Apollo (University of Cambridge) · doi:10.17863/cam.64000 · Cited by 902
AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications
Rashid, A. B., & Kausik, M. A. K. (2024) · Hybrid Advances · doi:10.1016/j.hybadv.2024.100277 · Cited by 764
Metadata
- Authors
- Mongan, J., Moy, L., & Kahn, C. E.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Kahn, Charles E.
- Venue
- Radiology: Artificial Intelligence
- Published
- 2020-03-01
- Volume
- 2
- Issue
- 2
- Pages
- e200029
- ISSN
- 2638-6100 (ISSN-L)
- PMC
- PMC8017414
- OpenAlex
- W3013294478
- Semantic Scholar
- 5b34da942633f3a3afe01386341b8839a985ca0d
- Cited by
- 1,443
- References
- 31
- Citations by year
- 2026: 381 · 2025: 233 · 2024: 287 · 2023: 220 · 2022: 170 · 2021: 120 · 2020: 29
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Radiology Artificial Intelligence (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Boettcher Foundation
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Library and Information Sciences › Academic Writing and Publishing
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- artificial intelligence in medical imaging, reporting guidelines, author guidelines, peer review, model validation, model generalization
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