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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“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.
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“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]
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“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]
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“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]
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“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]
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“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]
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“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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Cites
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CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
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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
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
AI applications to medical images: From machine learning to deep learning
Castiglioni, I., Rundo, L., Codari, M., Leo, G. D., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D’Amico, N. C., & Sardanelli, F. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.006 · Cited by 779
Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., et al. (2025) · BMJ · doi:10.1136/bmj-2024-081554 · Cited by 957
“…data, evaluation criteria and results, biases and other limitations, and periodic audits and updates 49-51 ; (c) a publication based on existing AI reporting the bmj | BMJ 2025;388:e081554 | doi: 10.1136/bmj-2024-081554 standards 13 15 52 ; and (d) a risk management file (see traceability 1).”
InfluentialSelf-citationBias in artificial intelligence algorithms and recommendations for mitigation
Nazer, L. H., Zatarah, R., Waldrip, S., Ke, J. X. C., Moukheiber, M., Khanna, A. K., Hicklen, R. S., Moukheiber, L., Moukheiber, D., Ma, H., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000278 · Cited by 621
“A number of comprehensive frameworks and checklists have been developed, such as the Translational Evaluation of Healthcare AI (TEHAI) [46], the DECIDE-AI [47], the Consolidated Standards of Reporting Trials-Artificial Intelligence (CONSORT-AI) [48], the prediction model risk of bias assessment tool (PROBAST) [49], an…”
MethodChecklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update
Tejani, A. S., Klontzas, M. E., Gatti, A. A., Mongan, J. T., Moy, L., Park, S. H., Kahn, C. E., Panel, F. T. C. 2. U., Abbara, S., Afat, S., et al. (2024) · Radiology: Artificial Intelligence · doi:10.1148/ryai.240300 · Cited by 570
Self-citationde Hond, A. A. H., Leeuwenberg, A. M., Hooft, L., Kant, I. M. J., Nijman, S. W. J., van Os, H. J. A., Aardoom, J. J., Debray, T. P. A., Schuit, E., van Smeden, M., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-021-00549-7 · Cited by 499
Ethical and Bias Considerations in Artificial Intelligence/Machine Learning
Hanna, M. G., Pantanowitz, L., Jackson, B., Palmer, O., Visweswaran, S., Pantanowitz, J., Deebajah, M., & Rashidi, H. H. (2025) · Modern Pathology · doi:10.1016/j.modpat.2024.100686 · Cited by 535
The current and future state of AI interpretation of medical images
Rajpurkar, P., & Lungren, M. P. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2301725 · Cited by 443
Kocak, B., Baessler, B., Bakas, S., Cuocolo, R., Fedorov, A., Maier-Hein, L., Mercaldo, N., Müller, H., Orlhac, F., Pinto dos Santos, D., et al. (2023) · Insights into Imaging · doi:10.1186/s13244-023-01415-8 · Cited by 471
“For example, CLAIM, being focused on artificial intelligence in medical imaging, lacks enough emphasis on the radiomic workflow and data sharing.”
MethodInfluentialAbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?
Ma, J., Zhang, Y., Gu, S., Zhu, C. G., Ge, C., Zhang, Y., An, X., Wang, C., Wang, Q., Liu, X., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3100536 · Cited by 429
“reach high accuracy, but also can generalize well on data from different sources [8], [9].”
BackgroundExternal Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review
Yu, A. C., Mohajer, B., & Eng, J. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210064 · Cited by 477
Tam, T. Y. C., Sivarajkumar, S., Kapoor, S., Stolyar, A. V., Polanska, K., McCarthy, K. R., Osterhoudt, H., Wu, X., Visweswaran, S., Fu, S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01258-7 · Cited by 424
Radiomics in Oncology: A Practical Guide
Shur, J. D., Doran, S., Kumar, S., Dafydd, D. A., Downey, K., O’Connor, J. P. B., Papanikolaou, N. K., Messiou, C., Koh, D., & Orton, M. R. (2021) · Radiographics · doi:10.1148/rg.2021210037 · Cited by 388
“To aid authors and to provide a framework for manuscript writing, there are various radiomic- and artificial intelligence–specific checklists, reporting guides, and radiomic quality scores that can be referred to (4,21,48,49), in addition to artificial intelligence extensions of familiar guidelines such as TRIPOD (Tra…”
BackgroundArtificial intelligence in dental research: Checklist for authors, reviewers, readers
Schwendicke, F., Singh, T., Lee, J., Gaudin, R., Chaurasia, A., Wiegand, T., Uribe, S. E., & Krois, J. (2021) · Journal of Dentistry · doi:10.1016/j.jdent.2021.103610 · Cited by 394
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Liu, J., Zhang, Y., Chen, J.-N., Xiao, J., Lu, Y., Landman, B. A., Yuan, Y., Yuille, A., Tang, Y., & Zhou, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01934 · Cited by 381
“, performance on new data across many hospitals, rather than the performance tailored to a single dataset [39, 42].”
BackgroundDetecting caries lesions of different radiographic extension on bitewings using deep learning
Ros, A. G. C., Gehrung, S., Krois, J., Chaurasia, A., Gomez-Rossi, J., Gaudin, R., Elhennawy, K., & Schwendicke, F. (2020) · Journal of Dentistry · doi:10.1016/j.jdent.2020.103425 · Cited by 353
Deep learning for caries detection: A systematic review
Mohammad‐Rahimi, H., Motamedian, S. R., Rohban, M. H., Krois, J., Uribe, S. E., Mahmoudinia, E., Rokhshad, R., Nadimi, M., & Schwendicke, F. (2022) · Journal of Dentistry · doi:10.1016/j.jdent.2022.104115 · Cited by 356
Koçak, B., D’Antonoli, T. A., Mercaldo, N. D., Alberich‐Bayarri, Á., Baeßler, B., Ambrosini, I., Андрейченко, А. Е., Bakas, S., Beets‐Tan, R. G. H., Bressem, K. K., et al. (2024) · Insights into Imaging · doi:10.1186/s13244-023-01572-w · Cited by 326
“MAIC-10 achieved a high correlation score to Check-list for Artificial Intelligence in Medical Imaging (CLAIM) [27], a widely used 42-item reporting checklist, despite being tested on a small number of publications from the journal in which it was published.”
BackgroundMethodSchachner, T., Keller, R., & v Wangenheim, F. (2020) · Journal of Medical Internet Research · doi:10.2196/20701 · Cited by 267
AI pitfalls and what not to do: mitigating bias in AI
Gichoya, J. W., Thomas, K., Celi, L. A., Safdar, N., Banerjee, I., Banja, J. D., Seyyed-Kalantari, L., Trivedi, H., & Purkayastha, S. (2023) · The British Journal of Radiology · doi:10.1259/bjr.20230023 · Cited by 277
“63,64 We encourage researchers to ensure that their work is reproducible, allowing for independent validation and bias detection.”
Reproducibility standards for machine learning in the life sciences
Heil, B. J., Hoffman, M. M., Markowetz, F. M., Lee, S., Greene, C. S., & Hicks, S. C. (2021) · Nature Methods · doi:10.1038/s41592-021-01256-7 · Cited by 261
Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)
Kelly, B. S., Judge, C., Bollard, S. M., Clifford, S. M., Healy, G. M., Aziz, A., Mathur, P., Islam, S., Yeom, K. W., Lawlor, A., et al. (2022) · European Radiology · doi:10.1007/s00330-022-08784-6 · Cited by 252
“The quality of research design and reporting varies, and guidelines such as those issued by CONSORT [26] and the RSNA [27] should be followed to improve the quality of research overall.”
ESMO guidance for reporting oncology real-world evidence (GROW)
Castelo-Branco, L., Pellat, A., Martins-Branco, D., Valachis, A., Derksen, J. W. G., Suijkerbuijk, K. P. M., Dafni, U., Dellaporta, T., Vogel, A., Prelaj, A., et al. (2023) · Annals of Oncology · doi:10.1016/j.annonc.2023.10.001 · Cited by 247
Nam, D., Chapiro, J., Paradis, V., Seraphin, T. P., & Kather, J. N. (2022) · JHEP Reports · doi:10.1016/j.jhepr.2022.100443 · Cited by 244
“The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and the assessment checklist developed by the Fairness, Universality, Traceability, Usability, Robustness and Explainability AI (FUTURE-AI) initiative establish reporting guidelines for appraisal of AI studies.(102) Similarly, the checklists for esta…”
MethodAccelerating antibiotic discovery through artificial intelligence
Melo, M. C. R., Maasch, J. R. M. A., & de la Fuente-Nunez, C. (2021) · Communications Biology · doi:10.1038/s42003-021-02586-0 · Cited by 234
Artificial intelligence: A critical review of applications for lung nodule and lung cancer
de Margerie‐Mellon, C., & Chassagnon, G. (2022) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2022.11.007 · Cited by 218
Shen, Y., Wu, N., Phang, J., Park, J., Liu, K., Tyagi, S., Heacock, L. C., Kim, S., Moy, L. P., Cho, K., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101908 · Cited by 222
Self-citationCurrent applications and development of artificial intelligence for digital dental radiography
Putra, R. H., Doi, C., Yoda, N., Astuti, E. R., & Sasaki, K. (2021) · Dentomaxillofacial Radiology · doi:10.1259/dmfr.20210197 · Cited by 222
Ning, Y., Teixayavong, S., Shang, Y., Savulescu, J., Nagaraj, V., Miao, D., Mertens, M., Ting, D. S. W., Ong, J. C. L., Liu, M., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00143-2 · Cited by 212
“As the TREGAI checklist focuses on ethical considerations of GenAI, research developing GenAI could use it in addition to existing model development guidelines (eg, TRIPOD 56,57 or CLAIM 58,59 ) and other tools to enhance ethical discussion (eg, the Ethical Operating System Toolkit 60 and the Organisation for Economic…”
MethodYoon, J. H., Strand, F., Baltzer, P., Conant, E. F., Gilbert, F. J., Lehman, C. D., Morris, E. A., Mullen, L. A., Nishikawa, R. M., Sharma, N., et al. (2023) · Radiology · doi:10.1148/radiol.222639 · Cited by 217
Self-citationDICE-Net: A Novel Convolution-Transformer Architecture for Alzheimer Detection in EEG Signals
Μιλτιάδους, Α., Gionanidis, E., Tzimourta, K. D., Γιαννακέας, Ν., & Tzallas, A. T. (2023) · IEEE Access · doi:10.1109/access.2023.3294618 · Cited by 209
“Radiology: Artificial Intelligence [62].”
InfluentialLiu, S., McCoy, A. B., & Wright, A. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf008 · Cited by 200
“…and LLMs in healthcare, including DECIDE-AI (Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence), 11 CLAIM (Checklist for Artificial Intelligence in Medical Imaging), 12 and CONSORT-AI (Consolidated Standards of Reporting Trials-AI).”
To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)
Omoumi, P., Ducarouge, A., Tournier, A., Harvey, H., Kahn, C. E., Verchère, F. L.-D., dos Santos, D. P., Kober, T., & Richiardi, J. (2021) · European Radiology · doi:10.1007/s00330-020-07684-x · Cited by 181
Self-citationFeigerlova, E., Hani, H., & Hothersall-Davies, E. (2025) · BMC Medical Education · doi:10.1186/s12909-025-06719-5 · Cited by 166
“A checklist for artificial intelligence in medical imaging (CLAIM) was used for studies including AI in radiology [34] (Supplementary Table 4).”
Radiomics, machine learning, and artificial intelligence—what the neuroradiologist needs to know
Wagner, M. W., Namdar, K., Biswas, A., Monah, S. R., Khalvati, F., & Ertl-Wagner, B. B. (2021) · Neuroradiology · doi:10.1007/s00234-021-02813-9 · Cited by 179
Wei, L., Niraula, D., Gates, E. D. H., Fu, J. Y., Luo, Y., Nyflot, M. J., Bowen, S. R., Naqa, I. M. E., & Cui, S. (2023) · British Journal of Radiology · doi:10.1259/bjr.20230211 · Cited by 179
“Mongan et al proposed a CLAIM guide 70 to promote clear, transparent and reproducible scientific communication about AI’s application in medical images.”
Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis
Park, S. H., Han, K., Jang, H. Y., Park, J. E., Lee, J.-G., Kim, D. W., & Choi, J. (2023) · Radiology · doi:10.1148/radiol.220182 · Cited by 163
Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology
Daneshjou, R., Barata, C., Betz‐Stablein, B., Celebi, M. E., Codella, N., Combalia, M., Guitera, P., Gutman, D. A., Halpern, A. C., Helba, B., et al. (2021) · JAMA Dermatology · doi:10.1001/jamadermatol.2021.4915 · Cited by 166
Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis
Kolbinger, F. R., Veldhuizen, G. P., Zhu, J., Truhn, D., & Kather, J. N. (2024) · Communications Medicine · doi:10.1038/s43856-024-00492-0 · Cited by 160
Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging
Sanaat, A., Shiri, I., Arabi, H., Mainta, I., Nkoulou, R. N., & Zaidi, H. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-05167-1 · Cited by 159
“…for not using cross-validation since recent guidelines seem to suggest that although multiple internal cross-validation can be useful, independent validation using an external dataset for a single trained model is preferred over internal validation to properly evaluate generalizability [25, 26].”
MethodYang, N., Ser, J. D., Walsh, S. L. F., Schönlieb, C., Roberts, M. T., SELBY, I. A., Howard, K., Owen, J., Neville, J., Guiot, J., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.01.001 · Cited by 161
Maleki, F., Ovens, K. L., Gupta, R., Reinhold, C., Spatz, A., & Forghani, R. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.220028 · Cited by 176
“published the CLAIM checklist to aid authors and reviewers with best practices in artificial intelligence research in medical imaging (14).”
BackgroundMethodREFORMS: Consensus-based Recommendations for Machine-learning-based Science
Kapoor, S., Cantrell, E. M., Peng, K., Pham, T. H., Bail, C. A., Gundersen, O. E., Hofman, J. M., Hullman, J. R., Lones, M. A., Malik, M. M., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk3452 · Cited by 255
“For example, the CLAIM checklist ( 35 ) provides best practices for reporting research on AI in medical imaging.”
InfluentialInterpretable machine learning for dementia: a systematic review
Martin, S. A., Townend, F. J., Barkhof, F., & Cole, J. H. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.12948 · Cited by 149
Drukker, K., Chen, W., Gichoya, J. W., Gruszauskas, N. P., Kalpathy-Cramer, J., Koyejo, S., Myers, K. J., Sá, R. C., Sahiner, B., Whitney, H. M., et al. (2023) · Journal of Medical Imaging · doi:10.1117/1.jmi.10.6.061104 · Cited by 149
Mitigating Bias in Radiology Machine Learning: 1. Data Handling
Rouzrokh, P., Khosravi, B., Faghani, S., Moassefi, M., Vera Garcia, D. V., Singh, Y., Zhang, K., Conte, G. M., & Erickson, B. J. (2022) · Radiology: Artificial Intelligence · doi:10.1148/ryai.210290 · Cited by 145
“…in Medical Imaging and items 1–5 of the Guiding Principles for Good Machine Learning Practice for Medical Device Development, recently introduced by the U.S. Food and Drug Administration, Health Canada, and the United Kingdom’s Medicines and Healthcare products Regulatory Agency (24,57).”
BackgroundCentral reading of ulcerative colitis clinical trial videos using neural networks
Gottlieb, K., Requa, J., Karnes, W., Chandra Gudivada, R., Shen, J., Rael, E., Arora, V., Dao, T., Ninh, A., & McGill, J. (2021) · Gastroenterology · doi:10.1053/j.gastro.2020.10.024 · Cited by 136
PRISMA AI reporting guidelines for systematic reviews and meta-analyses on AI in healthcare
Cacciamani, G. E., Chu, T. N., Sanford, D. I., Abreu, A., Duddalwar, V., Oberai, A., Kuo, C.-C. J., Liu, X., Denniston, A. K., Vasey, B., et al. (2023) · Nature Medicine · doi:10.1038/s41591-022-02139-w · Cited by 143
Self-citationIntroduction to radiomics for a clinical audience
McCague, C., Ramlee, S., Reinius, M., SELBY, I. A., Hulse, D., Piyatissa, P., Bura, V., Crispin‐Ortuzar, M., Sala, E., & Woitek, R. (2023) · Clinical Radiology · doi:10.1016/j.crad.2022.08.149 · Cited by 137
Momtazmanesh, S., Nowroozi, A. A., & Rezaei, N. (2022) · Rheumatology and Therapy · doi:10.1007/s40744-022-00475-4 · Cited by 149
“The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) [22] and the guideline released by the National Health Service (NHS) for ‘‘good practice for digital and data-driven health technologies’’ [192] are examples of such recommendations.”
Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects
Díaz, O., Rodríguez‐Ruiz, A., & Sechopoulos, I. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111457 · Cited by 152
A decade of radiomics research: are images really data or just patterns in the noise?
dos Santos, D. P., Dietzel, M., & Baeßler, B. (2020) · European Radiology · doi:10.1007/s00330-020-07108-w · Cited by 129
“Among the first steps, one possible approach could be to require authors and reviewers to follow checklists such as the aforementioned or the recently proposed Checklist for Artificial Intelligence in Medical Imaging [9].”
BackgroundRecent methodological advances in federated learning for healthcare
Zhang, F., Kreuter, D., Chen, Y., Dittmer, S., Tull, S., Shadbahr, T., Schut, M. C., Asselbergs, F. W., Kar, S., Sivapalaratnam, S., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101006 · Cited by 152
Hickman, S. E., Woitek, R., Le, E., Im, Y. R., Luxhøj, C. M., Avilés-Rivero, A. I., Baxter, G. C., Mackay, J., & Gilbert, F. J. (2021) · Radiology · doi:10.1148/radiol.2021210391 · Cited by 133
AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis
Chen, Y., Schönlieb, C., Lió, P., Leiner, T., Dragotti, P. L., Wang, G., Rueckert, D., Firmin, D. N., & Yang, G. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3141367 · Cited by 132
Preetha, C. J., Meredig, H., Brugnara, G., Mahmutoglu, M. A., Foltyn‐Dumitru, M., Isensee, F., Kessler, T., Pflüger, I., Schell, M., Neuberger, U., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00205-3 · Cited by 129
Environmental Sustainability and AI in Radiology: A Double-Edged Sword
Doo, F. X., Vosshenrich, J., Cook, T. S., Moy, L. P., Almeida, E. P. R. P., Woolen, S. A., Gichoya, J. W., Heye, T. J., & Hanneman, K. A. (2024) · Radiology · doi:10.1148/radiol.232030 · Cited by 135
Self-citationShamrat, F. M. J. M., Azam, S., Karim, A., Islam, M. R., Tasnim, Z., Ghosh, P., & Boer, F. D. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12050680 · Cited by 128
“Even patients might benefit from this technology [18,19].”
BackgroundMao, B., Zhang, L., Ning, P., Ding, F. H., Wu, F., Lu, G. G., Geng, Y., & Ma, J. (2020) · European Radiology · doi:10.1007/s00330-020-07056-5 · Cited by 122
Spadarella, G., Stanzione, A., D’Antonoli, T. A., Андрейченко, А. Е., Fanni, S. C., Ugga, L., Kotter, E., & Cuocolo, R. (2022) · European Radiology · doi:10.1007/s00330-022-09187-3 · Cited by 120
“Some editors and journals have already begun to move in this direction, and it is desirable for this trend to spread at least to the more visible publications in our field [68, 72, 73].”
BackgroundAdvancing health equity with artificial intelligence
Thomasian, N. M., Eickhoff, C., & Adashi, E. Y. (2021) · Journal of Public Health Policy · doi:10.1057/s41271-021-00319-5 · Cited by 117
“Regulators can look towards AI-reporting recommendations in academia as a reference when formulating validation criteria, as the research community has recently developed a number of checklists designed to quantify the robustness of artificial intelligence studies [24, 25].”
BackgroundCurrent challenges of implementing artificial intelligence in medical imaging
Saw, S. N., & Ng, K. H. (2022) · Physica Medica · doi:10.1016/j.ejmp.2022.06.003 · Cited by 117
“This checklist is supposed to be viewed as a “ best practice ” for authors and reviewers in future journal publications to improve the quality of AI papers in medical imaging [46].”
BackgroundMa, J., Zhang, Y., Gu, S., Ge, C., Mae, S., Young, A., Zhu, C., Yang, X., Meng, K., Huang, Z., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00154-7 · Cited by 145
Prostate MRI radiomics: A systematic review and radiomic quality score assessment
Stanzione, A., Gambardella, M., Cuocolo, R., Ponsiglione, A., Romeo, V., & Imbriaco, M. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109095 · Cited by 115
Brain metastasis detection using machine learning: a systematic review and meta-analysis
Cho, S. J., Sunwoo, L., Baik, S. H., Bae, Y. J., Choi, B. S., & Kim, J. H. (2020) · Neuro-Oncology · doi:10.1093/neuonc/noaa232 · Cited by 116
Generalizability of deep learning models for dental image analysis
Krois, J., Ros, A. G. C., Chaurasia, A., Patil, R., Chaudhari, P. K., Gaudin, R., Gehrung, S., & Schwendicke, F. (2021) · Scientific Reports · doi:10.1038/s41598-021-85454-5 · Cited by 130
Sadr, S., Mohammad‐Rahimi, H., Motamedian, S. R., Zahedrozegar, S., Motie, P., Vinayahalingam, S., Dianat, O., & Nosrat, A. (2022) · Journal of Endodontics · doi:10.1016/j.joen.2022.12.007 · Cited by 126
Jha, A. K., Bradshaw, T., Buvat, I., Hatt, M., Prabhat, K. C., Liu, C., Obuchowski, N., Saboury, B., Slomka, P. J., Sunderland, J. J., et al. (2022) · Journal of Nuclear Medicine · doi:10.2967/jnumed.121.263239 · Cited by 117
Equity in essence: a call for operationalising fairness in machine learning for healthcare
Wawira Gichoya, J., McCoy, L. G., Celi, L. A., & Ghassemi, M. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2020-100289 · Cited by 104
Hickman, S. E., Baxter, G. C., & Gilbert, F. J. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01333-w · Cited by 110
Deep learning for automated detection and numbering of permanent teeth on panoramic images
Estai, M., Tennant, M., Gebauer, D., Brostek, A., Vignarajan, J., Mehdizadeh, M., & Saha, S. (2021) · Dentomaxillofacial Radiology · doi:10.1259/dmfr.20210296 · Cited by 117
Hurkmans, C., Bibault, J.-E., Brock, K. K., van Elmpt, W., Feng, M., David Fuller, C., Jereczek-Fossa, B. A., Korreman, S., Landry, G., Madesta, F., et al. (2024) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2024.110345 · Cited by 110
Deep learning in pulmonary nodule detection and segmentation: a systematic review
Gao, C., Wu, L., Wu, W., Huang, Y., Wang, X., Sun, Z., Xu, M., & Gao, C. (2024) · European Radiology · doi:10.1007/s00330-024-10907-0 · Cited by 121
Javaheri, T., Homayounfar, M., Amoozgar, Z., Reiazi, R., Homayounieh, F., Abbas, E., Laali, A., Radmard, A. R., Gharib, M. H., Mousavi, S. A., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00399-3 · Cited by 129
Equity within AI systems: What can health leaders expect?
Gurevich, E., Hassan, B. E., & Morr, C. E. (2022) · Healthcare Management Forum · doi:10.1177/08404704221125368 · Cited by 108
Fraser, A. G., Biasin, E., Bijnens, B. H., Bruining, N., Caiani, E. G., Cobbaert, K., Davies, R. H., Gilbert, S., Hovestadt, L., Kamenjašević, E., et al. (2023) · Expert Review of Medical Devices · doi:10.1080/17434440.2023.2184685 · Cited by 116
“CLAIM Mongan 2020 Radiology: Artificial Intelligence [75] Radiology Artificial intelligence in medical imaging”
MethodBrady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Insights into Imaging · doi:10.1186/s13244-023-01541-3 · Cited by 106
“By monitoring for individual components of data drift, institutions could trigger re-evaluation of model performance depending on timing and severity of changes and initiate appropriate steps to safeguard patient care.”
BackgroundSelf-citationDeep learning in oral cancer- a systematic review
Warin, K., & Suebnukarn, S. (2024) · BMC Oral Health · doi:10.1186/s12903-024-03993-5 · Cited by 117
“…studies should address minimum standard guidelines, such as Standards for Reporting of Diagnostic Accuracy Study-AI (STARD-AI); standards for diagnostic studies using AI models [76], Checklist for Artificial Intelligence in Medical Imaging (CLAIM); and a checklist for AI in medical imaging [77].”
BackgroundMethodAI-driven deep CNN approach for multi-label pathology classification using chest X-Rays
Albahli, S., Rauf, H. T., Algosaibi, A. A., & Bălaş, V. E. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.495 · Cited by 95
“Even the patients could use this tool ( Mongan, Moy & Kahn, 2020 ; Ali et al., 2020 ; Tabares-Soto et al., 2020 ).”
MethodDeep learning: A primer for dentists and dental researchers
Mohammad‐Rahimi, H., Rokhshad, R., Bencharit, S., Krois, J., & Schwendicke, F. (2023) · Journal of Dentistry · doi:10.1016/j.jdent.2023.104430 · Cited by 99
Jacob, C., Brasier, N., Laurenzi, E., Heuss, S., Mougiakakou, S. G., Çöltekin, A., & Peter, M. K. (2024) · Journal of Medical Internet Research · doi:10.2196/67485 · Cited by 125
“…[14], Standards for Reporting of Diagnostic Accuracy Studies–Artificial Intelligence [22], Checklist for Artificial Intelligence in Medical Imaging [23], Prediction Model Risk of Bias Assessment Tool–Artificial Intelligence [24], and others, a unified international consensus on the evaluation of…”
Requirements and reliability of AI in the medical context
Balagurunathan, Y., Mitchell, R., & Naqa, I. E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.024 · Cited by 94
“More specific recommendations for radiological application have been proposed, such as the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) [59].”
MethodAutonomous Chest Radiograph Reporting Using AI: Estimation of Clinical Impact
Plesner, L. L., Müller, F. C., Nybing, J. D., Laustrup, L. C., Rasmussen, F., Nielsen, O. W., Boesen, M., & Andersen, M. B. (2023) · Radiology · doi:10.1148/radiol.222268 · Cited by 89
“In recent years, there has been massive attention toward image classification with deep learning–based artificial intelligence (AI) models for radiology (2–4).”
Detecting white spot lesions on dental photography using deep learning: A pilot study
Askar, H., Krois, J., Rohrer, C., Mertens, S., Elhennawy, K., Ottolenghi, L., Mazur, M., Paris, S., & Schwendicke, F. (2021) · Journal of Dentistry · doi:10.1016/j.jdent.2021.103615 · Cited by 96
Orlhac, F., Nioche, C., Klyuzhin, I. S., Rahmim, A., & Buvat, I. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.06.007 · Cited by 98
Reproducibility in machine‐learning‐based research: Overview, barriers, and drivers
Semmelrock, H., Ross‐Hellauer, T., Kopeinik, S., Theiler, D., Haberl, A., Thalmann, S., & Kowald, D. (2025) · AI Magazine · doi:10.1002/aaai.70002 · Cited by 118
Reporting Use of AI in Research and Scholarly Publication—JAMA Network Guidance
Flanagin, A., Pirracchio, R., Khera, R., Berkwits, M., Hswen, Y., & Bibbins-Domingo, K. B. (2024) · JAMA · doi:10.1001/jama.2024.3471 · Cited by 100
Enhancing trust in AI through industry self-governance.
Roski, J., Maier, E. J., Vigilante, K., Kane, E. A., & Matheny, M. E. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab065 · Cited by 83
Soffer, S., Morgenthau, A. S., Shimon, O., Barash, Y., Konen, E., Glicksberg, B. S., & Klang, E. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.05.014 · Cited by 86
Translation of AI into oncology clinical practice
Naqa, I. E., Karolak, A. M., Luo, Y., Folio, L. R., Tarhini, A. A., Rollison, D. E. M., & Parodi, K. (2023) · Oncogene · doi:10.1038/s41388-023-02826-z · Cited by 85
“● Lack of adherence to the checklists mentioned above (TRIPOD-AI, CLAIM, MI-CLAIM, CLAMP, etc.) or more accurately, there is also lack of consensus standardizations among these standards, which creates its own confusion.”
BackgroundInfluentialChaudhari, A. S., Sandino, C. M., Cole, E. K., Larson, D. B., Gold, G. E., Vasanawala, S. S., Lungren, M. P., Hargreaves, B. A., & Langlotz, C. P. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27331 · Cited by 84
Artificial Intelligence in Periodontology: A Scoping Review
Scott, J., Biancardi, A., Jones, O. P. H., & Andrew, D. (2023) · Dentistry Journal · doi:10.3390/dj11020043 · Cited by 88
“Whilst some efforts have been made to standardise methodologies [83,84], these are still yet to be adopted or referenced in wider practice.”
BackgroundDesai, A., Calivá, F., Iriondo, C., Mortazi, A., Jambawalikar, S., Bağcı, U., Perslev, M., Igel, C., Dam, E. B., Gaj, S., et al. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021200078 · Cited by 19
Zhong, J., Hu, Y., Si, L., Jia, G., Xing, Y., Zhang, H., & Yao, W. (2020) · European Radiology · doi:10.1007/s00330-020-07221-w · Cited by 78
“Other guidelines concerning artificial intelligence method also provide valuable references for RQS improvement [50, 51].”
BackgroundDetection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis
Din, M., Agarwal, S., Grzeda, M. T., Wood, D. A., Marc Modat, A., & Booth, T. C. (2022) · Journal of NeuroInterventional Surgery · doi:10.1136/jnis-2022-019456 · Cited by 89
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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