The DOI matches its Crossref record; 22 of the 23 references it cites were found in a registry or an index; 3 of the 4 sources its claims cite were checked for support.
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.
Open accessAbstract
Here we present the MI-CLAIM checklist, a tool intended to improve transparent reporting of AI algorithms in medicine.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
3 Partly supported · 2 Own interpretation · 1 No basis given · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Furthermore, the same methods that are enabling new analyses of clinical data may also enable the re-identification of patients in sometimes unpredictable ways17.”
§ Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist > General principles of the MI-CLAIM design
What the sources say
- Partly supportedcites [17] Schwarz, C. G. et al. N. Engl. J. Med. 381, 1684–1686 (2019). sourceExperimental — accuracy not yet measured
The paper reports that face-recognition software matched photographs to reconstructed cranial MRI faces for 70 of 84 participants, demonstrating a potential route to re-identification. It does not establish that the same methods enable new analyses of clinical data or describe the risk as unpredictable. (LLM, conf=0.82)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Newer methodologies have the danger of becoming more complex and less interpretable, even when sophisticated interpretation technique are used11.”
What the sources say
- Partly supportedcites [11] Lipton, Z. C. Queue 16, 31–57 (2018). sourceExperimental — accuracy not yet measured
The discussion of simulatability (§3.1.1, p. 4) links complexity to difficulty understanding a model, and the discussion of post-hoc interpretability (§3.2, p. 5) says such techniques often do not reveal precisely how a model works. But §4.1 (p. 7) cautions that deep models are not uniformly less interpretable than simpler ones and can have advantages for some post-hoc interpretations. The paper supports the stated risk, not a general trend that newer methodologies are less interpretable. (LLM, conf=0.84)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In the past, ‘minimum information’ guidelines have substantially improved the downstream utility, transparency, and interpretability of data deposited in repositories and reported in publications across many other research domains, including data on randomized control trials15, RNA (gene) expression, diagnostic accuracy15, observational studies16, and meta-analyses.”
What the sources say
- Partly supportedcites [15] Moher, D. et al. Br. Med. J. 340, c869 (2010). sourceExperimental — accuracy not yet measured
In the section on improving RCT reporting, the paper identifies CONSORT as a checklist of essential reporting items. Its closing discussion says CONSORT seems to have improved reporting in journals that adopted it and notes that other groups use its template for diagnostic tests. It does not establish improved utility of data deposited in repositories or demonstrate that diagnostic-accuracy reporting has improved. (LLM, conf=0.84)
- Not checked — no full text of the sourcecites [16] von Elm, E. et al. Ann. Intern. Med. 147, 573–577 (2007). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unclear documentation on training and test-cohort selection, development methodology, and how systems were validated has added to the confusion.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 2
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Real-world circumstances may prevent the complete sharing of code in certain situations, such as for results published by commercial entities that view their code base as a proprietary trade secret.”
§ Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist > The six parts of the MI-cLAIM > Part 6: reproducible pipeline
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, no single examination or interpretation methodology is perfect.”
§ Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist > The six parts of the MI-cLAIM > Part 5: model examination
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper relies on this without support.
Overfitting and information leakage are the strongest drivers of poor generalization out of sample and of misrepresentations of model efficacy.
“These two factors are the strongest drivers of poor generalization out of sample and misrepresentations of model efficacy.”
The paper relies on this without support.
Testing with an external cohort from an independent clinic or hospital system is the highest level of validation.
“Testing with an external cohort from an independent clinic or hospital system is the highest level of validation.”
The paper relies on this without support.
Cross-validation is not a replacement for a separate test cohort.
“Cross-validation is not a replacement for a separate test cohort and should not be described as such.”
The paper relies on this without support.
Machine-learning models in other fields have shown serious shortcomings when researchers have attempted to generalize across populations.
“as these models in other fields have shown serious shortcomings when researchers have attempted to generalize across populations.”
The paper relies on this without support.
Sharing raw clinical data is often not possible because of institutional patient-privacy policies.
“Sharing of raw clinical data is often neither possible, due to institutional patient-privacy policies, nor advisable without such safeguards in place.”
The paper relies on this without support.
Validation of exact results is generally less important than whether the results are validated in a new cohort of patients.
“In any case, validation of the exact results is generally of less interest than whether or not the results are validated in a new cohort of patients.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Code for the complete model building pipeline should be published in well-documented scripts with computer environment requirements when possible7,8,13,14,16,19,20,24,25,27,33,50,62,64,65, including statements about any restrictions to access or re-use.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [7]
The cited source says what the claim attributes to it.
In the citing paper
“To ensure the generalizability of the AIPM to the intended healthcare setting, developers are advised to extensively validate the model for representative data from that setting6–8,10,13–15,24,27,56,64,66,68,72,77,79,87,88 (see phase 1, Representativeness).”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [7]
In the citing paper
“The subsequent evidence review underscored the limits of current knowledge and research about health care algorithms in the literature.”
From Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care (2023) · cites this paper as [11]
In the citing paper
“It has been widely recommended to report any measures taken to prevent overfitting3,7,8,13,15.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [7]
In the citing paper
“When possible, the literature recommends the comparison of current best practice (e.g., an existing prediction model or medical decision rule) to the AIPM performance7,8,14–16.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [7]
In the citing paper
“Performance analysis by population subgroups or specific problematic use cases is recommended to identify algorithmic bias6,7,23,24,26,42,61,72,79,91.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [7]
In the citing paper
“The literature recommends to assess generalizability on external data from a different time period, place, or healthcare setting3,7,8,10,16,17,27,56,68,72,79.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [7]
In the citing paper
“Medical AI models should not only have high performance, but should also be interpretable 28,29 .”
From Swarm learning for decentralized artificial intelligence in cancer histopathology (2022) · cites this paper as [29]
In the citing paper
“The MI-CLAIM (minimum information about clinical artificial intelligence modeling) checklist is available as Supplementary Table 7 (ref.29 ).”
From Swarm learning for decentralized artificial intelligence in cancer histopathology (2022) · cites this paper as [29]
Citations
Cites
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“…to a conventional medical machine learning workflow, the study was reported according to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines [14] and reinforced following the Minimum Information for Clinical Artificial Intelligence Modelling (MI-CLAIM) protocol [15].”
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“We also followed the minimum information about clinical artificial intelligence modelling (MI-CLAIM) checklist to provide transparent reporting of our DL model (Figure S1) (Norgeot et al., 2020).”
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“As stated, black-box machine-learning models lack interpretability and may fail for unknown reasons when tested on new populations [19].”
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Rahate, K., Sengar, M. S., & Patel, S. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-2238-3.ch006 · Cited by 7
Saha, P., De, A., Roy, S. D., & Bhowmik, M. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18953-y · Cited by 6
“Machine learning (ML) approaches are effective enough to identify complex imaging patterns from CVD images [1–4].”
Farook, T. H., Haq, T. M., Ramees, L., & Dudley, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-67640-3 · Cited by 7
“The University of Adelaide Human Research Ethics Committee (H-2022-185) granted approval for this study which also adhered to the Minimum Information for Clinical Artificial Intelligence Modelling (MI-CLAIM) 2021 protocol checklist 7 .”
MethodArtificial intelligence in the classification and segmentation of fundus images with choroidal nevi
Crump, T., Mohammed, E. A., Biglarbeiki, M., et al. (2024) · Canadian Journal of Ophthalmology · doi:10.1016/j.jcjo.2024.07.009 · Cited by 5
van Dijk, D. P. J., Volmer, L., Brecheisen, R., Dolan, R. D., Bryce, A. S., Chang, D. K.-F., McMillan, D. C., Stoot, J. H. M. B., West, M. A., Rensen, S. S., et al. (2023) · medRxiv · doi:10.1101/2023.04.23.23288981 · Cited by 5
“Additionally, the quantitative performance of DLNNs need to be comprehensively evaluated with external test datasets sourced from a wholly independent clinical workflow and a separate clinical setting from the one used to train the DLNN.8 In previous work, the DLNN that is the subject of this paper had been independen…”
MethodInfluentialPan, S., Fu, Y., Jiang, L. W., Liu, J., Cai, G., Li, W., Liu, W., Wang, X., Yin, Z., Hong, Q., et al. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106052 · Cited by 4
Unprepared and overwhelmed: A case for clinician-focused AI education
Siddiqui, N., Bouchi, Y., Kim, E., Hron, J. D., Park, J. S., & Kang, J. J. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103252 · Cited by 4
Reinforcement Learning to Prevent Acute Care Events Among Medicaid Populations: Mixed Methods Study
Basu, S., Muralidharan, B., Sheth, P., Wanek, D., Morgan, J., & Patel, S. Y. (2025) · JMIR AI · doi:10.2196/74264 · Cited by 4
“Analyses followed the Minimum Information About Clinical Artificial Intelligence Modeling checklist (MI-CLAIM) [16] and Developmental and Exploratory Clinical Investigations of Decision support systems driven by Artificial Intelligence (DECIDE-AI) guidelines [17].”
Arısan, A., Genç, C., & Duran, G. S. (2025) · Bioengineering · doi:10.3390/bioengineering12111165 · Cited by 4
“Our method not only benchmarks model performance but also offers a clinically relevant and accessible way to explore real-world applications, including remote monitoring and decision-support systems [8,24,25].”
Ivanovic, V., Sujan, M. A. J., Mengshoel, O. J., & Moholdt, T. T. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106158 · Cited by 7
Mohammad‐Rahimi, H., Ramani, R. S., Setzer, F. C., Schwendicke, F., Pauwels, R., & Nosrat, A. (2026) · International Endodontic Journal · doi:10.1111/iej.70094 · Cited by 5
Huang, C., Fang, W., Chen, K.-H., Lin, S.-Y., Ho, C. K., Hwang, J., Tai, T., Liu, Y., Shih, C., Chen, J., et al. (2026) · Osteoporosis International · doi:10.1007/s00198-026-08067-6 · Cited by 4
“Statement 1 highlights the rationale: many Asia-Pacific patients who could benefit from osteoporosis assessment are missed under current practice, and unregulated AI use could lead to inconsistent standards [23, 24].”
Alam, S. S., Ahmed, S., Farook, T. H., & Dudley, J. (2024) · Oral · doi:10.3390/oral4030032 · Cited by 6
Li, N., Shi, Y., Zhao, Y., & Zhan, S. (2025) · Intelligent Medicine · doi:10.1016/j.imed.2025.07.002 · Cited by 4
Patiño, G. A., & Roberts, L. W. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005793 · Cited by 4
Lim, C. Y., Sohn, B., Seong, M., Kim, E. Y., Kim, S. T., & Won, S. Y. (2024) · Yonsei Medical Journal · doi:10.3349/ymj.2024.0007 · Cited by 5
Silverthorn, M., Yakubu, S. O., & Falconer, L. (2025) · PLOS Sustainability and Transformation · doi:10.1371/journal.pstr.0000155 · Cited by 4
“Checklists are emerging as an important transparency tool in many research areas [61–63].”
Mitigation of outcome conflation in predicting patient outcomes using electronic health records
Reincke, S. M., Espinosa, C. A., Chung, P., James, T. K., Berson, E., & Aghaeepour, N. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf033 · Cited by 4
Saria, S., Schulam, P., Yeh, B. J., et al. (2021) · Critical Care Explorations · doi:10.1097/cce.0000000000000441 · Cited by 4
“This report adheres to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis and minimum information about clinical artifical intelligence modeling checklists for improved reporting of prediction model development and validation (13, 14).”
MethodSelf-citationThe Strategic Efficacy of Artificial Intelligence (AI) in Medical Tourism
Gbobaniyi, O., Tincani, D., & Emelone, P. (2024) · Advances in hospitality, tourism and the services industry (AHTSI) book series · doi:10.4018/979-8-3693-2248-2.ch005 · Cited by 4
Pattathil, N., Lee, T.-S. J., Huang, R. S., Lena, E. R., & Felfeli, T. (2024) · Graefe s Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-024-06553-3 · Cited by 5
“…models, MI-CLAIM (Minimum Information about Clinical Artificial Intelligence Modelling) focuses on technical reproducibility of clinical AI modeling research, and MINIMAR (MINimum Information for Medical AI Reporting) provides guidance on proper data source usage and model evaluation [16, 17].”
Using preregistration as a tool for transparent fNIRS study designPreprint
Schroeder, P. A., Artemenko, C., Kosie, J. E., Cockx, H., Stute, K., Pereira, J., Klein, F., & Mehler, D. M. A. (2022) · doi:10.31222/osf.io/zfawx · Cited by 4
Wrightson, J. G., Lasinsky, A. M., Snell, R. R., Hogel, M., Mota, A., Khan, K. M., & Ardern, C. L. (2024) · medRxiv · doi:10.1101/2024.05.29.24308137 · Cited by 4
“The methods are reported in accordance with the relevant requirements of the Minimum Information about Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist [16] .”
Artificial Intelligence in Mental Healthcare
Jafar, A., Shabnam, S., & Khan, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5072-1.ch014 · Cited by 3
Cavicchioli, M., Moglia, A., Garret, G., Puglia, M., Vacavant, A., Pugliese, G., & Cerveri, P. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110530 · Cited by 3
Roberts, A., Zhang, J., Tozlu, C., Romano, D., Akkuş, S., Kim, H., Sabuncu, M. R., Spincemaille, P., Li, J., Wang, Y., et al. (2025) · Neurosurgery · doi:10.1227/neu.0000000000003721 · Cited by 3
Hogg, H. D. J., Talks, J. S., Engelmann, J., Teare, M. D., Pogose, M., Patel, P. J., Balaskas, K., Maniatopoulos, G., & Keane, P. A. (2025) · Eye · doi:10.1038/s41433-025-04025-4 · Cited by 3
“This study is reported in line with the MI-CLAIM checklist (Supplementary Table 1) [18].”
A Review on SPECT Myocardial Perfusion Imaging Attenuation Correction Using Deep Learning
Apostolopoulos, I. D., Papandrianos, N. I., Papageorgiou, E. I., & Apostolopoulos, D. J. (2025) · Applied Sciences · doi:10.3390/app152011287 · Cited by 5
Bracher‐Smith, M., & Escott‐Price, V. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03330-4 · Cited by 5
Kernbach, J. M., Schroeder, U., Hakvoort, K., Ort, J., Hamou, H. A., Bzdok, D., Temel, Y., Kubben, P., Weyland, C. S., Wiesmann, M., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01183-2 · Cited by 4
Murray, B., Zhao, B., Chen, Z., Smith, S. E., Kong, Y., Shen, Y., Li, S., Chen, X., Sikora, A. N., & the MRC‐ICU Investigator Team (2026) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1002/phar.70090 · Cited by 3
“The reporting of this study adheres to the STrengthening and reporting of OBservational data in Epidemiology (STROBE) statement and Minimal Information about Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist (Appendix I) [24, 25].”
Olawade, D. B., Osborne, A., Soladoye, A. A., Oluwadare, O. E., Awogbindin, E., & Wada, O. Z. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106313 · Cited by 3
Möller, M., Wild, E., Tan, W., & Schreyögg, J. (2026) · Value in Health · doi:10.1016/j.jval.2026.01.013 · Cited by 3
A web-based deep learning cascade for automated detection and quantification of marginal bone loss
Adnan, N., Ahmed, S. M. F., Nooruddin, A., Sadiq, A., Khalid, A., Haseeb, M., Ghori, M. H., & Umer, F. (2026) · BMC Oral Health · doi:10.1186/s12903-026-07962-y · Cited by 3
“This experiment is reported adhering to the Minimum Information about Clinical Artificial Intelligence Modeling: the MI-CLAIM checklist [15].”
Montero, A. M. B., Huet‐Dastarac, M., Herranz-Hernández, S. M., Tengler, B., Buhl, E. S., Galapon, A., Cárdenas, C. E. S., Fusella, M., Herbin, G., de Hond, Y. J. M., et al. (2026) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2026.100940 · Cited by 4
Bozkurt, S., Fereydooni, S., Kar, İ., Chalmers, C. D., Leslie, S., Pathak, R., Walling, A. M., Lindvall, C. J., Lorenz, K., Quest, T. E., et al. (2024) · JMIR Research Protocols · doi:10.2196/56353 · Cited by 4
“These questions are a combination of those from the Minimum Information for Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist [11] and additional ones formulated by our authorship team.”
Clinical and translational mode of single‐cell measurements: An artificial intelligent single cell
Wang, X., Powell, C. A., Ma, Q., et al. (2024) · Clinical and Translational Discovery · doi:10.1002/ctd2.353 · Cited by 3
Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology
Alapati, R., Renslo, B., Wagoner, S. F., Karadaghy, O. A., Serpedin, A., Kim, Y. E., Feucht, M., Wang, N. C., Ramesh, U. R., Bon‐Nieves, A. A., et al. (2024) · The Laryngoscope · doi:10.1002/lary.31756 · Cited by 3
Trung, K. V., Hollenbach, M., Veldhuizen, G. P., Saldanha, O. L., Garbe, J., Rosendahl, J., Krug, S., Michl, P., Feisthammel, J., Karlas, T., et al. (2024) · Digestion · doi:10.1159/000543049 · Cited by 3
“Our study rigorously adheres to the Minimum information about clinical arti fi cial intelligence modeling (MI-CLAIM) standards, ensuring a comprehensive and systematic approach to reporting and conducting medical image analysis research [18].”
Clinical Machine Learning Modeling Studies: Methodology and Data Reporting
Dumitrașcu, O., Wang, Y., & Chen, J. J.-W. (2022) · Journal of Neuro-Ophthalmology · doi:10.1097/wno.0000000000001605 · Cited by 3
Huang, A. W., Haslberger, M., Coulibaly, N., Galárraga, O., Oganisian, A., Belbasis, L., & Panagiotou, O. A. (2022) · Diagnostic and Prognostic Research · doi:10.1186/s41512-022-00119-9 · Cited by 7
Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey
Li, R., Sharma, V., Thangamani, S., & Yakimovich, A. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.912809 · Cited by 3
“Several minimalistic quality standards applicable to the clinical setting have been proposed (Hernandez-Boussard et al., 2020; Mongan et al., 2020; Norgeot et al., 2020), and while coming from slightly different perspectives they demonstrate an overlap on essential topics like the dataset…”
BackgroundMethodElmahdy, M., & Sebro, R. (2023) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2023.100113 · Cited by 4
Human intelligence for authors, reviewers and editors using artificial intelligence
Palacios, M. (2023) · Colombia medica · doi:10.25100/cm.v54i3.5867 · Cited by 4
Pedersen, M., Pardoe, H., de Weger, A., Hutchison, D. M., Abbott, D., Verspoor, K. M., & Jackson, G. D. (2023) · doi:10.31219/osf.io/kag75 · Cited by 3
Lai, S. M., Malik, A. M., Sathe, T. S., Silvestri, C. J., Manji, G. A., & Kluger, M. D. (2024) · medRxiv · doi:10.1101/2024.09.20.24314053 · Cited by 3
Exploring the Interpretability of AI Decision Support Systems for Surgical Anatomy RecognitionPreprint
Khan, D. Z., Adams, T., Wijekoon, A., Herrera, R. R., Bano, S., McCulloch, P. G., Stoyanov, D. V., Clarkson, M. J., Costanza, E., BLANDFORD, A. E., et al. (2026) · medRxiv · doi:10.64898/2026.06.02.26354729 · Cited by 3
Li, L., Verma, M., Wang, B., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000174 · Cited by 2
“A quality checklist [27] for the proposed deep learning method is shown in Table 3.”
MethodPertaining analysis of fracture risk in Osteoporotic patients using Machine Learning Techniques
Neeta, T. R., & Poonia, R. C. (2023) · IEEE International Conference on Contemporary Computing and Communications (InC4) · doi:10.1109/inc457730.2023.10262867 · Cited by 2
“MI-CLAIM(Minimum Information about Artificial Intelligence Modeling) checklist was compared to verify the quality of the articles[3].”
MethodAskar, M., Småbrekke, L., Holsbø, E., Bongo, L. A., & Svendsen, K. (2024) · Exploratory Research in Clinical and Social Pharmacy · doi:10.1016/j.rcsop.2024.100463 · Cited by 2
“41 After we aggregated each pair of health codes on the patient level, a network that represents the multimorbidity patterns in the dataset population was created.”
BackgroundImoto, Y., Inui, T., Matsui, K., Watanabe, Y., Igari, T., Takeuchi, S., Kimura, M., Yagi, S., & Kawano, H. (2025) · Cureus · doi:10.7759/cureus.89446 · Cited by 3
Foundations of Artificial Intelligence in Hepatology: What a Clinician Needs to Know?
Peng, N., Song, S. J., Hui, V. W., Lai, J. C., Wong, G. L., Wong, V. W., & Yip, T. C. (2025) · Journal of Clinical and Experimental Hepatology · doi:10.1016/j.jceh.2025.103183 · Cited by 4
Fochesato, A., Brooks, L., Bazgir, O., Pierrillas, P. B., Jamois, C., Lu, J. J.-Q., & Mercier, F. (2025) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.70113 · Cited by 4
Silvestri, C. J., Hoagland, D. L., Woodward, J. M., Naaseh, A., McDermott, C. E., Carter, B. M., Lund, S. B., Prokop, L. J., Bernard, A., & Navarro, S. M. (2025) · Journal of surgical education · doi:10.1016/j.jsurg.2025.103758 · Cited by 2
Yang, M., Luo, Y., He, T., Diao, S., Li, H., Zou, K., Hazlewood, G. S., Vandvik, P. O., Kawano-Dourado, L. B., Dourado, D. D. A., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02156-2 · Cited by 6
Kim, M., Kim, K. E., Kwon, J.-H., Han, J.-Y., Kim, J. H., & Kim, M. G. (2025) · Therapeutic Advances in Drug Safety · doi:10.1177/20420986251405082 · Cited by 2
Metadata
- Authors
- Norgeot, B., Quer, G., Beaulieu-Jones, B. K., Torkamani, A., Dias, R., Gianfrancesco, M., Arnaout, R., Kohane, I. S., Saria, S., Topol, E., Obermeyer, Z., Yu, B., & Butte, A. J.
- Affiliations
- University of California, San Francisco (US)
- Scripps Research Institute (US)
- Johns Hopkins University (US)
- University of California, Berkeley (US)
- Corresponding author
- Butte, Atul J.
- ORCID
- Venue
- Nature Medicine
- Published
- 2020-09
- Volume
- 26
- Issue
- 9
- Pages
- 1320-1324
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC7538196
- OpenAlex
- W3083804794
- Semantic Scholar
- ac9bfb4ef6a1b3db3143ccf5394edc1171725234
- Cited by
- 665
- References
- 23
- Citations by year
- 2026: 165 · 2025: 112 · 2024: 128 · 2023: 107 · 2022: 89 · 2021: 58 · 2020: 6
- FWCI
- 19.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K08HL125945, R01HL150394)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (K01AR075085)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- clinical AI, reporting guidelines, algorithmic transparency, reporting quality
- MeSH terms
- Machine Learning, Arthritis, Rheumatoid, Artificial Intelligence, Diabetic Retinopathy, Humans, Drug-Related Side Effects and Adverse Reactions
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.
A language model drew the claims from the paper on 2026-10-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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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