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]
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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Cites
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CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
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Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD Initiative.
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Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative
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A Critical Review of Recurrent Neural Networks for Sequence LearningPreprint
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Improving the Quality of Reporting of Randomized Controlled Trials: The CONSORT Statement
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STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration
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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
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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
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Items 901–1,000 of 1,001
Huh, H., & Park, J. J. (2026) · Diagnostics · doi:10.3390/diagnostics16060858 · Cited by 3
“Reporting should specify where the algorithm runs, what data are stored, and how performance is monitored, consistent with reporting extensions for artificial intelligence interventions and diagnostic accuracy studies [28,29,32–34].”
InfluentialDinç, R., & Ardıç, N. (2026) · Bioengineering · doi:10.3390/bioengineering13040399 · Cited by 2
“The CLAIM guideline for imaging-centric AI-CAD studies provide structured reporting elements for dataset description, reference standards, performance evaluation, and reproducibility considerations [16,28].”
Deep Learning and Cardiovascular Diseases: An Updated Narrative Review
Myśliwiec, A., Bartusik‐Aebisher, D., Xavierselvan, M., Paul, A., & Aebisher, D. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15083053 · Cited by 5
Hildebrand, L., Clarke, L. M., White, S. J., Perperidis, A., Gorayski, P., Doig, M., & To, M. (2026) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2026.112021 · Cited by 2
Decomposing performance inflation in radiomics: factorial quantification of analytical leakage
García-Hidalgo, C., Hernández, J. A. C., Espí, J. V. C., Vicente, G. P., Ponce, J. F. M., Olmos, A. V., & Martínez, J. M. P. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.112890 · Cited by 2
Tshivhula, N., Mbunge, E., & Makaba, T. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03624-9 · Cited by 2
“Alignment with reporting standards such as PRISMA 2020, TRIPOD+AI, CLAIM, and the Radiomics Quality Score should be a baseline expectation rather than an exception [14, 17, 18, 107].”
InfluentialGete, K. Y., Idris, Z. N., & Ayele, B. A. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.06.007 · Cited by 2
Artificial Intelligence in Imaging of Chronic Liver Diseases
Sabottke, C. F., Spieler, B. M., Moawad, A. W., & Elsayes, K. M. (2021) · Magnetic Resonance Imaging Clinics of North America · doi:10.1016/j.mric.2021.05.011 · Cited by 2
Dikici, E., Nguyen, X. V., Takacs, N., & Prevedello, L. M. S. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106901 · Cited by 2
Artificial intelligence: is it more accurate than endodontists in root canal therapy?
Murad, M., & Tamimi, F. (2023) · Evidence-Based Dentistry · doi:10.1038/s41432-023-00901-8 · Cited by 2
Perspective: A resident’s role in promoting safe machine-learning tools in sleep medicine
Smith, C. M., & Vendrame, M. (2023) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.10724 · Cited by 2
Wang, C., Li, J., Wu, C., Wu, Y., Tay, J., Wu, M., Hsu, C.-H., Liu, Y.-K., Chen, C., & Huang, C. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00929-3 · Cited by 2
“The study results are reported according to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) [11].”
MethodZhou, J., Wen, Y., Ding, R., Liu, J., Fang, H., Li, X., Zhao, K., & Wan, Q. (2024) · Cancer Imaging · doi:10.1186/s40644-024-00660-4 · Cited by 2
Abuhasanein, S., Edenbrandt, L., Enqvist, O., Jahnson, S., Leonhardt, H., Trägårdh, E., Ulén, J., & Kjölhede, H. (2024) · Scandinavian Journal of Urology · doi:10.2340/sju.v59.39930 · Cited by 2
“The study is a retrospective observational case-control study, reported according to CLAIM (Checklist for AI in Medical Imaging) [11].”
MethodEichner classification based on panoramic X-ray images using deep learning: A pilot study
Otsuka, Y., Indo, H. P., Kawashima, Y., Tanaka, T., Kono, H., & Kikuchi, M. (2024) · Bio-Medical Materials and Engineering · doi:10.3233/bme-230217 · Cited by 2
Deep learning-based correction for time truncation in cerebral computed tomography perfusion
Ichikawa, S., Ozaki, M., Itadani, H., Sugimori, H., & Kondo, Y. (2024) · Radiological Physics and Technology · doi:10.1007/s12194-024-00818-6 · Cited by 2
“This manuscript was prepared based on CLAIM guidelines [15].”
Deo, N., Nawaz, F. A., du Toit, C., Tran, T., Mamillapalli, C. K., Mathur, P., Reddy, S., Visweswaran, S., Prabhu, T. N., Moidu, K., et al. (2024) · Exploration of Digital Health Technologies · doi:10.37349/edht.2024.00018 · Cited by 2
Pan, Z., Qin, Y., Bai, W., He, Q., Yin, X., & He, J. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02321-3 · Cited by 2
“This stands in contrast to the Checklist for Artificial Intelligence in Medical Imaging [14] and the Standards for Reporting of Diagnostic Accuracy Studies [15] guidelines, which both explicitly mandate the transparent reporting of missing data and the strategies employed to address them.”
ResultCrns: CLIP-driven referring nuclei segmentation
Yuan, R., Zhang, W., Dong, X., & Zhang, W. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06692-8 · Cited by 2
Tian, K., Zhao, C., Yang, Y., Liu, P., Mossa‐Basha, M., Levitt, M. R., Zhai, D., Liu, D., Li, Z., Liu, Y., et al. (2025) · Journal of NeuroInterventional Surgery · doi:10.1136/jnis-2024-022784 · Cited by 2
“18 The study design is illustrated in online supplemental figure 1.”
A Primer on Artificial Intelligence Research in Dentistry
Uribe, S. E., & Schwendicke, F. (2025) · Springer eBooks · doi:10.1007/978-3-031-84047-0_9 · Cited by 2
Williams, S. C., Duvaux, D., Das, A., Sinha, S., Horsfall, H. L., Funnell, J. P., HANRAHAN, J. G., Khan, D. Z., Muirhead, W. R., Kitchen, N. D., et al. (2025) · Neurosurgery · doi:10.1227/neu.0000000000003466 · Cited by 2
Image analysis research in neuroradiology: bridging clinical and technical domains
Pareto, D., Naval‐Baudin, P., Pons‐Escoda, A., Bargalló, N., Garcia-Gil, M., Majós, C., & Rovira, À. (2025) · Neuroradiology · doi:10.1007/s00234-025-03633-x · Cited by 2
“To mitigate methodological biases that can significantly affect the performance of artificial intelligence models, the CLAIM guidelines have been proposed [32].”
Shang, T., Sun, M., & Tang, S. (2024) · International Conference on Mechanical and Electronics Engineering · doi:10.1109/icmee63700.2024.11025346 · Cited by 2
“• Dilated Convolutions: Three different dilation rates (6, 12, and 18) are used in the module.”
Performance of a Chest Radiograph–based Deep Learning Model for Detecting Hepatic Steatosis
Ueda, D., Uchida‐Kobayashi, S., Yamamoto, A., Walston, S. L., Motoyama, H., Fujii, H., Watanabe, T., Miki, Y., & Kawada, N. (2025) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.240402 · Cited by 2
Deep learning for osteoporosis screening in dental practice: a systematic review
Querrer, R., Vieira, L. S., Teodoro, A. B., de Melo, N. S., Mesquita, C. R. M., Silva, M. A. G., Figueiredo, P. T. D. S., & Leite, A. F. (2025) · Dentomaxillofacial Radiology · doi:10.1093/dmfr/twaf052 · Cited by 2
Warin, K., Limprasert, W., Paipongna, T., Chaowchuen, S., & Vicharueang, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-06318-w · Cited by 3
“This study was performed under the guidelines of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) protocol 13 .”
Dagli, M. M., Sussman, J. H., Gujral, J., Budihal, B. R., Kerr, M., Yoon, J. W., Ozturk, A. K., Cahill, P. J., Anari, J. B., Winkelstein, B. A., et al. (2025) · Acta Neurochirurgica · doi:10.1007/s00701-025-06645-x · Cited by 2
Wright, L. S. (2023) · Cambridge University Press eBooks · doi:10.1017/9781009052023.006 · Cited by 2
Le, V. N. T., Kim, J., Yang, Y.-M., & Lee, D. (2021) · Applied Sciences · doi:10.3390/app11209688 · Cited by 2
“Recently, the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) has been developed based on the consensus of radiological experts and is viewed as the best guideline for presenting research [19].”
BackgroundMethodInfluentialBridge, J., Meng, Y., Zhu, W., FitzMaurice, T. S., McCann, C. A., Addison, C. A., Wang, M., Merritt, C., Franks, S., Mackey, M., et al. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1113030 · Cited by 2
“In this work, we aim to address the problems associated with previous models by following guidelines for the reporting (17, 18) and development (19) of prediction models to ensure that we have rigorous documentation allowing the methods developed here to be replicated.”
Deep learning for prediction of radiation-induced oral mucositis: Need for longitudinal studies
Gupta, A., & Rangarajan, K. (2023) · Cancer Research Statistics and Treatment · doi:10.4103/crst.crst_263_23 · Cited by 2
The Era of Artificial Intelligence in Radiology: How to Prepare for a Different Future
Rouzrokh, P., & Awan, O. A. R. (2024) · Academic Radiology · doi:10.1016/j.acra.2023.12.022 · Cited by 2
Segmentation and Volumetric Analysis of Heart from Cardiac CT Images
Rashmitha, Manjunath, K. N., Kulkarni, A., & Kulkarni, V. (2024) · Cardiovascular Engineering and Technology · doi:10.1007/s13239-024-00715-4 · Cited by 2
“These are the recommended [14–17] and widely used metrices.”
Automated diagnosis of autism: State of the artPreprint
Valizadeh, A., Moassefi, M., Nakhostin-Ansari, A., Some’eh, S. H., Hosseini-Asl, S. H., Torbati, M. S., Aghajani, R., Ghorbani, Z. M., Oskouie, I. M., Aghajani, F., et al. (2021) · medRxiv · doi:10.1101/2021.06.29.21254249 · Cited by 2
Advancing radiomics research translation through a public database
Ko, H. S. (2023) · European Radiology · doi:10.1007/s00330-023-10284-0 · Cited by 2
Nishio, M., Matsunaga, T., Matsuo, H., Nogami, M., Kurata, Y., Fujimoto, K., Sugiyama, O., Akashi, T., Aoki, S., & Murakami, T. (2023) · medRxiv · doi:10.1101/2023.12.01.23299267 · Cited by 2
“This study was conducted in accordance with the Checklist for Artificial Intelligence in Medical Imaging (14).”
MethodOmoumi, P., & Richiardi, J. (2024) · Radiology · doi:10.1148/radiol.233299 · Cited by 2
Performance of GPT-4 on the American College of Radiology In-Service ExaminationPreprint
Payne, D. L., Purohit, K., Borrero, W. M., Chung, K., Hao, M., Mpoy, M., Jin, M. X., Prasanna, P., & Hill, V. B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.15.580546 · Cited by 2
Zancan, B. A. G. (2023) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.59.2023.tde-20022024-135415 · Cited by 2
Fuster-Matanzo, A., Picó, A., Bataller, F. B., Jiménez-Pastor, A., Weiss, G. J., Martí‐Bonmatí, L., Sanchez, A. D. L., Banna, G. L., Addeo, A., & Alberich‐Bayarri, Á. (2024) · medRxiv · doi:10.1101/2024.05.31.24308261 · Cited by 2
Adhikary, P. K., Singh, S., Singh, S., Choudhary, R., Saxena, C., Chauhan, P., Sharma, P., Singh, S. M., Gupta, S., Deb, K. S., et al. (2025) · medRxiv · doi:10.1101/2025.06.25.25330252 · Cited by 2
Redefining Radiology - Artificial Intelligence Integration in Medical Imaging
Iffath, T., Athkuri, P., Nadeem, M., & Goyal, P. (2024) · doi:10.47275/0032-745x-410 · Cited by 2
Leo, L. M., Prabha, B. S., Gopinath, K., Tamilselvi, C., Mohanaprakash, T. A., & Naveen, P. (2023) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia60377.2023.10426597 · Cited by 1
Desiraju, B. K., Thiruvengadam, R., Wadhwa, N., Khurana, A., Papageorghiou, A. T., Noble, J. A., & Bhatnagar, S. (2024) · F1000Research · doi:10.12688/f1000research.152426.1 · Cited by 1
Deciphering the Symptom Spectrum: A Comprehensive Analysis of Migraine Patterns and Types
Hasan, F., Khatun, R., Salman, E., Mahmud, T., Raza, D. M., & Nahid, A. H. (2024) · American Journal of Data Mining and Knowledge Discovery · doi:10.11648/j.ajdmkd.20240901.12 · Cited by 1
Semi-supervised Label Generation for 3D Multi-modal MRI Bone Tumor Segmentation
Curto, A., Schloßmacher, B., Valle, C., Gersing, A. S., Neumann, J., von Eisenhart‐Rothe, R., Rueckert, D., & Hinterwimmer, F. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01448-z · Cited by 1
“This article follows the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) [25] guidelines.”
Krishna, N. S., Garza-Frias, E., Dasegowda, G., Kaviani, P., Karout, L., Fahimi, R., Bizzo, B. C., Dreyer, K. J., Kalra, M. K., & Digumarthy, S. R. (2025) · Radiation Protection Dosimetry · doi:10.1093/rpd/ncaf018 · Cited by 1
Asiares, A. V., Vitadello, T., Velarde, O. M., Schachoff, S., Ibrahim, T., & Nekolla, S. G. (2025) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2025.03.003 · Cited by 1
Yan, R., Zhang, X., Cao, Q., Xu, J., Chen, Y., Qin, S., Zhang, S., Zhao, W., Xing, X., Yang, W., et al. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70012 · Cited by 2
Pardo, J. C. C., Zapaishchykova, A., Boyd, A., Tak, D., Zielke, J., Mahootiha, M., Ye, Z., Vajapeyam, S., Jones, J., Smith, C., et al. (2025) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdaf145 · Cited by 1
“32 The study includes patients with radiographically suspected and clinically diagnosed pLGG diagnosed between 1992 and 2020 who underwent initial MR surveillance for at least one year prior to biopsy or treatment pooled from two institutional datasets: Dana-Faber Cancer Institute/Boston Children’s…”
Putra, R. H., Astuti, E. R., Nurrachman, A. S., Savitri, Y., Vadya, A. V., Khairunisa, S. T., & Iikubo, M. (2025) · Oral Radiology · doi:10.1007/s11282-025-00839-w · Cited by 1
“While CNNs can automatically identify critical image features, ensuring well-justified dataset sizes and balanced data distributions is crucial for improving model performance [44–46].”
Clinical Validation in AI for Healthcare: An Experiential Learning
Nabhani‐Gebara, S., Zacharias, L., Martí‐Bonmatí, L., Galiana-Bordera, A., Colantonio, S., Chouvarda, I., & Elkhoury, G. (2025) · Springer eBooks · doi:10.1007/978-3-031-89963-8_12 · Cited by 2
Zhang, L. J., Chen, Q., Tang, C. X., Shi, Z., Liu, T., Hu, C., Lü, B., Jin, Z., Lu, G., Cao, D., et al. (2025) · Intelligent Medicine · doi:10.1016/j.imed.2024.11.006 · Cited by 1
Gracea, R. S., Chopra, S., Östgren, P., Benchimol, D., & Jacobs, R. (2025) · International Journal of Dentistry · doi:10.1155/ijod/1932229 · Cited by 1
You, J., Ou, H., Zhang, Y., Wu, X., Zhang, L., Jin, Z., Chen, Q., Shen, H., Liu, X., Sun, J., et al. (2025) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2025.111223 · Cited by 1
Liu, X., Ren, H., Lv, J., Wang, L., Wu, M. C., & Zheng, C. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112509 · Cited by 1
Bridging AI and Regulation: Large Language Models for Documentation Compliance Check
Tran, Q., Salg, J., Shpileuskaya, K., Wang, Q., Putzar, L., & Blankenburg, S. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229064 · Cited by 1
“Furthermore, because of the lack of standardised requirements for AI system documentation, we have adopted the guidelines proposed by Mongan et al. [28].”
Colliander, E., Tupper, S., Kielberg, M. L., Liu, M. L., Almar-Munoz, E., Mayr, A., & Mombiela, R. M. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14228255 · Cited by 1
“The quality of the selected studies was rigorously assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) [13] alongside the Quality Assessment of Diagnostic Accuracy Studies, Version 2 (QUADAS-2) [14].”
Automatic detection of urinary stones from non-contrast enhanced computed tomography images
Lee, J., Jang, D., Jeon, Y.-J., Kim, Y. J., Ahn, H., Choi, W. S., Kang, B., Yoon, Y. E., Lee, D. K., & Oh, J. (2025) · Urolithiasis · doi:10.1007/s00240-025-01902-1 · Cited by 1
“This study adhered to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) [12], and all procedures followed the principles outlined in the Declaration of Helsinki.”
Revolutionizing Liver Imaging: Artificial Intelligence–Driven Advances in Diagnostics and Staging
Devkota, S., Bhujade, H., & Kalra, N. (2025) · Journal of Clinical and Experimental Hepatology · doi:10.1016/j.jceh.2025.103439 · Cited by 1
Al-Khanaty, A., Hennes, D. M. Z. B., Guduguntla, A., Guerrero, P., Delgado, C., Dinneen, E. P., Mazzone, E., Appu, S., Bolton, D., Eapen, R. S., et al. (2025) · Cancers · doi:10.3390/cancers18010028 · Cited by 1
“…outputs that highlight the imaging regions contributing to the prediction; these principles align with established reporting and transparency frameworks such as the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) [65] and foundational work on explainable machine learning [66].”
AI in Managing Systemic Conditions
Pham, T. D., Holmes, S., Chatzopoulou, D., & Coulthard, P. (2026) · Springer eBooks · doi:10.1007/978-3-032-11531-7_11 · Cited by 1
Systematic review of emerging artificial intelligence research in glioma diagnosis and therapy
Karavolias, I., & Mammis, A. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00640-y · Cited by 1
“Such attempts have been made by proposing unified evaluation tools [78].”
Sakai, Y., Kim, J., Phi, H. Q., Hu, A. C., Balali, P., Guggenberger, K. V., Woo, J. H., Bos, D., Kasner, S., Cucchiara, B. L., et al. (2026) · Journal of Neuroimaging · doi:10.1111/jon.70119 · Cited by 1
“Reporting followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [ 16 ] and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) [ 17 ] guidelines.”
Mesquita, P. F., Pinho, R. T., Rodrigues, A., Costa, C., Teixeira, P., Ferreira, R., Ponte, A., & Freitas, T. (2026) · Digestive and Liver Disease · doi:10.1016/j.dld.2025.12.032 · Cited by 1
Isaac, A., Klontzas, M. E., Amoretti, N., Grewal, J., Bauones, S., Matković, A., Drobny, D., Filippiadis, D. K., & Dalili, D. (2026) · Seminars in Musculoskeletal Radiology · doi:10.1055/a-2736-1861 · Cited by 1
Zhu, K., Samsudin, N. H., & Qu, H. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08311-0 · Cited by 2
“This aligns with emerging needs for point-of-care diagnostics in stroke management [49].”
Maruyama, H., Toyama, Y., Araki, Y., Takanami, K., Ito, M., Nakajima, Y., Takase, K., & Kamei, T. (2026) · JMIR Cancer · doi:10.2196/86630 · Cited by 1
“We adhered to the guidelines outlined in the checklist for AI in medical imaging to ensure methodological transparency and ethical rigor [19].”
Artificial Intelligence and CT Neuroimaging in Dementia and Psychotic Disorders: A Viewpoint
Bampton, T. J., Weber, A., Bastiampillai, T. J., Palmer, L. J., & Clark, S. R. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-01852-z · Cited by 1
Ashkan, A., Behnaz, M., Taramsari, A. R., Farimani, R. M., Radmard, B., Farhadian, M., & Motie, P. (2026) · International Orthodontics · doi:10.1016/j.ortho.2026.101156 · Cited by 1
Focusing on Data to Improve Machine Learning-Guided Antibiotic Discovery
Ta, W., Naiberg, R., Yoon, C. H., Pao, S. Y., Tindall, J., Liu, G., & Stokes, J. M. (2026) · Microbial Drug Resistance · doi:10.1177/10766294261431279 · Cited by 1
Akib, M. M. R., Rahaman, H., & Akter, R. (2026) · International Conference on Image Processing and Capsule Networks (ICIPCN) · doi:10.1109/icipcn67432.2026.11438428 · Cited by 1
“Multi-center evaluation, calibration analysis under shift, and clinician-in-the-loop deployment are essential next steps to ensure safety, transparency, and reproducibility in real-world clinical adoption [18], [19],[20].”
Ganss, C., & Vach, K. (2026) · Caries Research · doi:10.1159/000551765 · Cited by 1
Frisina, P., Ricci, P., Valentini, F., & Messineo, D. (2026) · Journal of Imaging · doi:10.3390/jimaging12040151 · Cited by 1
“Future studies that develop and validate prediction models should follow established reporting guidelines and checklists (e.g., CLAIM and TRIPOD-AI) and include external validation and reproducibility analyses (e.g., ICC-based feature stability) [45,46].”
López-Medina, D. C., Oliveros, A., Moreno-Ángel, N., Arellano, A. C. H., & Henao-Pérez, M. (2026) · Journal of Medical Internet Research · doi:10.2196/78168 · Cited by 1
“Although this heterogeneity is informative for evidence mapping, it limits direct comparability across sources [15,34, 40,45,46].”
InfluentialFrom Proof‐of‐Concept to Clinical Practice: Reproducibility in Medical Imaging AI
Norris, S. A., & Badawy, M. K. (2026) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.70088 · Cited by 1
He, Y., Lu, Y., & Hu, L. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1781422 · Cited by 1
Saloum, A., Zaher, I., Stipho, C. T., Demir, E., Naravetla, V., Pahlevani, M., Yaghi, N. K., & Karsy, M. J. (2026) · BioMedInformatics · doi:10.3390/biomedinformatics6020020 · Cited by 1
Sekkat, H., Madkouri, Y., Khallouqi, A., Talbi, M., Mouden, O. E., Halimi, A., Rhazouani, O. E., Merabet, Y. E., & Bentayeb, F. (2026) · Emergency Radiology · doi:10.1007/s10140-026-02465-3 · Cited by 1
Dawa, H., Aroso, C. M., Vinhas, A. S., Mendes, J. M., & Cortes, A. R. G. (2026) · Applied Sciences · doi:10.3390/app16083739 · Cited by 2
“Adoption of established reporting guidelines—TRIPOD for prediction models [53] and CLAIM for imaging AI [54]—would substantially improve evidence quality and cross-study comparability.”
Stansch, J., Trung, K. V., Blank, V., Kather, J. N., Herzog, M., Seibel, T., Koop, P., Haase, R., Berg, T., Wiegand, J., et al. (2026) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2026.03.007 · Cited by 1
Qu, H., Li, J., Gao, Y., Xu, X.-M., Zhang, X.-B., & Yang, S.-D. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1807400 · Cited by 1
Accurate Asthma-COPD Overlap Classification via Deep Transfer Learning in Medical Image Segmentation
Ye, W., Mo, D., & Yang, Y. (2026) · International Journal of COPD · doi:10.2147/copd.s578685 · Cited by 1
Vinay, V., Jodalli, P. S., Chavan, M. S., Satyarup, D., Bhor, K. B., & Buddhikot, C. S. (2026) · International Journal of Dentistry · doi:10.1155/ijod/6730710 · Cited by 1
Shimizu, E., Yokoiwa, R., & Nakayama, S. (2026) · Applied Sciences · doi:10.3390/app16094448 · Cited by 1
Wang, F., & Lyu, Y. (2026) · BioMedical Engineering OnLine · doi:10.1186/s12938-026-01583-w · Cited by 1
“This investigation was performed in accordance with the ethical standards outlined in the Declaration of Helsinki (2013 revision) and adhered to the STARD and CLAIM guidelines for reporting diagnostic accuracy and artificial intelligence studies in medical imaging [33].”
Echeverri-Hoyos, J., Echeverri-Franco, J. A., Bonilla, N., Monsalve-Morales, G., & Tuta-Quintero, E. (2026) · Current Oncology · doi:10.3390/curroncol33050287 · Cited by 1
Peer review: historical evolution and reviewers’ learning
Gómez, M. P. A., & Sardanelli, F. (2026) · Insights into Imaging · doi:10.1186/s13244-026-02302-8 · Cited by 1
Hu, J., Huang, Y., Zhang, Q., Hu, Y., Zhao, Y., Zhang, X., Fan, W., Zheng, H., Liang, D., Hu, Z., et al. (2026) · EJNMMI Physics · doi:10.1186/s40658-026-00889-x · Cited by 1
“Other emerging applications of AI in PET/CT include automated detection, diagnosis support, and task-specific reconstruction in oncologic and neurologic imaging [21–23].”
Sulaimanov, U., Şanlıer, N., Moniri, A., Demir, B., Serikkanov, Y. S., BAYRAMOĞLU, A. R., Al-Jebur, M. S., Sanlier, M. Y., Erginoğlu, U., Ötleş, E., et al. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15114230 · Cited by 1
“However, adherence remains inconsistent, and extracting even well-defined variables from these articles demands a degree of judgment that may challenge current models [10,11].”
AI-Based Post-processing for Artefact Mitigation in Radiography: A Systematic Review
Norris, S. A., Marland, H., Stephenson, D., Anderson, A., Uribe, S., & Badawy, M. K. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02037-4 · Cited by 1
Li, Q., Li, Y., Bai, J., Zhang, J., Nie, Z., Ren, Y., Li, Z., Guo, J., Li, M. J., Zhang, T., et al. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1841260 · Cited by 1
Alshreef, B. S. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02507-9 · Cited by 1
Wang, Z., Zhang, Y. J., Wang, Y., Xue, F., Yang, Y., & Zhao, W. (2026) · Frontiers in Pediatrics · doi:10.3389/fped.2026.1832066 · Cited by 1
Translational barriers to digital twins in radiation oncology
Mastroleo, F., Borras-Osorio, M., Patel, S. P., Routman, D. M., Moseley, D. J., Shiraishi, S., Foong, A., & Waddle, M. R. (2026) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2026.101024 · Cited by 1
“Sixth, use existing reporting and appraisal tools such as TRI-POD + AI, PROBAST, and, when decision-support interfaces are being tested, DECIDE-AI or CLAIM [28 – 31].”
Deep Learning in Dental Imaging: Advances, Challenges, and Future
AYDIN, E. C., & Can, Z. (2026) · Oral Radiology · doi:10.1007/s11282-026-00939-1 · Cited by 1
“…definition and model out - put is particularly critical in dentistry, where subtle lesions, anatomical superimposition, and device-dependent or protocol-dependent variability can yield strong aggregate performance metrics without ensuring reliable behavior in clinically borderline cases [ 22 ].”
García-Hidalgo, C., Hernández, J. A. C., Espí, J. V. C., Vicente, G. P., Ponce, J. F. M., & Olmos, A. V. (2026) · European Radiology · doi:10.1007/s00330-026-12703-4 · Cited by 1
Moncher, M., Traboulsi, V., Kofler, F., Müller, S., Steinbauer, F., & von See, C. (2026) · Journal of Imaging · doi:10.3390/jimaging12070285 · Cited by 1
“Therefore, the reported results cannot be interpreted as evidence of external robustness or clinical generalizability [16,21,23–25].”
Regulation of Artificial Intelligence in Veterinary Medicine
Winter, M. D. (2026) · Veterinary Clinics of North America Small Animal Practice · doi:10.1016/j.cvsm.2026.04.002 · Cited by 1
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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