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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“…Explainability Artificial Intelligence solutions) [62], (4) MI-CLAIM (Minimum Information about Clinical Artificial Intelligence Modeling) [63], (5) MINIMAR (Minimum Information for Medical AI Reporting) [64], (6) CLAIM (CheckList for Artificial Intelligence in Medical imaging) [65], (7)…”
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“Similarly, the minimum information about clinical arti fi cial intelligence modeling (MI-CLAIM) checklist was developed to improve transparency in model development, evaluation, and reporting [26].”
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“We did our best throughout the paper to adhere to the MI-CLAIM checklist [34] to ensure the transparency and reproducibility of our study.”
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“For reference, an AI modeling checklist can be used, which cat-egorizes the level of sharing on a 4-tier scale from fully open sharing to no sharing [22].”
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“During the development, implementation, and reporting, we adhered to the Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) guidelines [51], following best practices designed to promote transparency and reproducibility of our AI model.”
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“MI-CLAIM [28] Minimum Information about CLinical Artificial Intelligence Modeling This improves the reporting of information regarding clinical AI algorithms.”
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Callender, T., & van der Schaar, M. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00054-7 · Cited by 13
Sehring, J., Dohmen, H., Selignow, C., Schmid, K., Grau, S., Stein, M., Uhl, E., Mukhopadhyay, A., Németh, A., Amsel, D., et al. (2023) · Cancers · doi:10.3390/cancers15215190 · Cited by 13
“The documentation of this work is compliant with the recommendations and guidelines for reporting minimum information about clinical artificial intelligence modeling (MI-CLAIM) as proposed by Norgeot et al. [38].”
MethodKoçak, B., Keleş, A., & Köse, F. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.232604 · Cited by 12
“…Imaging (CLAIM), 42,43 Fair-ness, Universality, Traceability, Usability, Ro-bustness, and Explainability-AI (FUTURE-AI), 44 Minimum Information about Clinical AI Mod-elling (MI-CLAIM), 45 CheckList for EvaluAtion of Radiomics research (CLEAR), 46 and METh-odological RadiomICs Score (METRICS).”
Dong, Z., Tao, X., Du, H., Wang, J., Huang, L., He, C., Zhao, Z., Mao, X., Ai, Y., Zhang, B., et al. (2023) · Journal of Gastroenterology · doi:10.1007/s00535-023-02025-3 · Cited by 12
Pungitore, S. A., & Subbian, V. (2023) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-023-00143-4 · Cited by 17
Salybekov, A. A., Wolfien, M., Hahn, W., Hidaka, S., & Kobayashi, S. (2024) · Biomedicines · doi:10.3390/biomedicines12030606 · Cited by 16
“In section two, it is essential to partition model training and model testing [67].”
BackgroundMethodInfluentialA comprehensive review of techniques for documenting artificial intelligence
Königstorfer, F. (2024) · Digital Policy Regulation and Governance · doi:10.1108/dprg-01-2024-0008 · Cited by 15
Computer Vision and Videomics in Otolaryngology–Head and Neck Surgery
Paderno, A., Bedi, N., Rau, A., & Holsinger, F. C. (2024) · Otolaryngologic Clinics of North America · doi:10.1016/j.otc.2024.05.005 · Cited by 15
Lee, W. V., Song, Y., Chun, J. S., Ko, M., Jang, H. Y., Kim, I., Park, S., Lee, H., Lee, H., Kwak, S. H., et al. (2024) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2024.111897 · Cited by 12
Nasef, D., Nasef, D., Sher, M., & Toma, M. (2025) · Algorithms · doi:10.3390/a18060343 · Cited by 12
Oh, J.-H., Kim, H., Lee, K. M., & Ryu, C. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110369 · Cited by 11
Moving towards reproducible machine learning
(2021) · Nature Computational Science · doi:10.1038/s43588-021-00152-6 · Cited by 14
Yang, W., Wong, H. J., Fu, C. E., Rojas-Carabali, W. A., Agrawal, R. V., & Ang, B. C. H. (2025) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2025.06.006 · Cited by 12
Challenges and Potential of Artificial Intelligence in Neuroradiology
Winder, A. J., Stanley, E. A. M., Fiehler, J., & Forkert, N. D. (2024) · Clinical Neuroradiology · doi:10.1007/s00062-024-01382-7 · Cited by 12
“…checklist for artificial intelligence in medical imaging (CLAIM) [21], minimum information about clinical artificial intelligence modeling (MI-CLAIM) [22], t reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) [23], and prediction model risk of bias…”
Hurkmans, C., Bibault, J., Clementel, E., Dhont, J., van Elmpt, W., Kantidakis, G., & Andratschke, N. H. (2024) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2024.110196 · Cited by 11
Motmaen, I., Xie, K., Schönbrunn, L., Berens, J., Grunert, K., Plum, A. M., Raufeisen, J., Ferreira, A. F., Hermans, A., Egger, J., et al. (2024) · Clinical Oral Investigations · doi:10.1007/s00784-024-05781-5 · Cited by 11
“The study was approved by the Ethics Committee of the University Hospital RWTH Aachen (approval number EK 068/21, chairs: Prof. Dr. G. Schmalzing and PD Dr. R. Hausmann, approval date 25.02.2021) and followed the MI-CLAIM reporting guideline for the development of AI models [30].”
MethodInfluentialClinical Evaluation of Artificial Intelligence-Enabled Interventions
Hogg, H. D. J., Martindale, A. P. L., Liu, X., & Denniston, A. K. (2024) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.65.10.10 · Cited by 12
“26 At the time of writing, an AI-specific extension, STARD-AI, is under development.”
BackgroundArtificial intelligence methods available for cancer research
Murmu, A., & Győrffy, B. (2024) · Frontiers of Medicine · doi:10.1007/s11684-024-1085-3 · Cited by 15
Zhang, Y., Huang, Y., Rosen, A., Jiang, L. G., McCarty, M., RoyChoudhury, A., Han, J. H., Wright, A. T., Ancker, J. S., & Steel, P. A. D. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000606 · Cited by 10
Population-scale cross-sectional observational study for AI-powered TB screening on one million CXRs
Munjal, P., Mahrooqi, A. A., Rajan, R., Jeremijenko, A. M., Ahmad, I., Akhtar, M. I., Pimentel, M. A. F., & Khan, S. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01832-7 · Cited by 11
AI-Powered Precision Oncology: Computational Insights Redefining Therapeutic Landscapes
Tippur, A. (2023) · DHR Proceedings · doi:10.47488/dhrp.v3is1.95 · Cited by 12
Bias and Oversight in Clinical AI: A Review of Decision Support Tools and Equity Frameworks
Adegunle, F., Chhatwal, K., Arab, S., Alabdaljabar, M. S., Raslan, M. A., Sayed, O., & Goldsweig, A. M. (2026) · Journal of General Internal Medicine · doi:10.1007/s11606-026-10229-5 · Cited by 13
“Examples include SPIRIT-AI 90 , CONSORT-AI 91 , TRIPOD+AI 92 , PROBAST+AI 93 , MINIMAR 94 , MI-CLAIM 95 , DECIDE-AI 96 , and FUTURE-AI 97 .”
Maru, S., Matthias, M. D., Kuwatsuru, R., & Simpson, R. J. (2024) · Journal of Medical Internet Research · doi:10.2196/57750 · Cited by 11
Impact of artificial intelligence on future clinical pharmacy research and scholarship
Chan, A., Baker, W. L., Abazia, D. T., Bauman, J. L., DeVane, C. L., Goodlet, K. J., Hall, N., Hicks, J. K., Jones, E., Lu, C., et al. (2025) · JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY · doi:10.1002/jac5.70003 · Cited by 11
Dahlin, E. (2025) · AI & Society · doi:10.1007/s00146-025-02429-0 · Cited by 13
The state of the art in artificial intelligence and digital pathology in prostate cancer
Ni, H. M., Kouzy, R., Sabbagh, A., Rooney, M. K., Feng, J., Castillo, S. P., Gadoue, S. M., Kouzi, Z. E., Hoffman, K. E., Yuan, Y., et al. (2025) · Nature Reviews Urology · doi:10.1038/s41585-025-01070-2 · Cited by 13
Towards a framework for interoperability and reproducibility of predictive models
Rahrooh, A., Garlid, A. O., Bartlett, K., Coons, W. R., Petousis, P., Hsu, W. W. Y., & Bui, A. A. T. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104551 · Cited by 10
Iskrov, G. G., Raycheva, R., Kostadinov, K. R., Gillner, S., Blankart, C. R., Gross, E. S., Gumus, G., Митова, Е., Stefanov, S. A., Стефанов, Г., et al. (2024) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-024-03047-7 · Cited by 9
mlf-core: a framework for deterministic machine learning
Heumos, L., Ehmele, P., Cuellar, L. K., Menden, K., Miller, E., Lemke, S., Gabernet, G., & Nahnsen, S. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad164 · Cited by 11
Maktabi, H., Mater, A. E., Takroni, G. S. A., et al. (2023) · Annals of dental specialty · doi:10.51847/xmc7f5am6c · Cited by 9
Zapaishchykova, A., Tak, D., Ye, Z., Liu, K. X., Likitlersuang, J., Vajapeyam, S., Chopra, R., Seidlitz, J., Bethlehem, R. A. I., Mak, R. H., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00114 · Cited by 10
“…reviewing the pre-existing literature, we found no pediatric brain age models with implementable codes ( He et al., 2020; Hong et al., 2020; Hu et al., 2021; Mendes et al., 2021), which is critical to moving the field forward and investigating these models’ clinical utility ( Norgeot et al., 2020).”
BackgroundŞengün, B., İşcan, Y., Yazıcı, Z. A., et al. (2024) · Acta chirurgica Belgica · doi:10.1080/00015458.2024.2363599 · Cited by 9
Machine learning in shoulder arthroplasty
Schneller, T., Kraus, M., Schätz, J., Moroder, P., Scheibel, M. T., & Lazaridou, A. (2025) · Bone & Joint Open · doi:10.1302/2633-1462.62.bjo-2024-0234.r1 · Cited by 9
Bertram, C., Schutten, M. M., Ressel, L., Breininger, K., Webster, J. D., & Aubreville, M. (2025) · Veterinary Pathology · doi:10.1177/03009858251344320 · Cited by 9
“10 To improve the reporting quality of studies, specific reporting guidelines for AI research have been developed for some medical disciplines 10,13,15,22 and computer science.”
Kolossváry, M., Smith, E. E., Reeves, M. J., Layton, A. T., Mills, N. L., Motwani, M., Goto, S., & Atkins, N. (2025) · Circulation · doi:10.1161/circulationaha.125.074325 · Cited by 9
Sardar, S., Dash, S., Roychowdhury, P., Solanki, J., Sikdar, P., Thangappan, J., & Dutta, S. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag269 · Cited by 12
“Governance frameworks including MI-CLAIM, MI-CLAIM-GEN, TRIPOD + AI, and DECIDE-AI collectively address reporting trans-parency and lifecycle monitoring, yet none has been specifically adapted for AMR requirements [ 140– 143].”
Wang, J., Chavda, V. P., Prajapati, R., Bedse, A. P., Patel, J., Popat, S., Jogi, G., Nalla, L. V., Jetha, K. L., Shen, B., et al. (2023) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2023.100159 · Cited by 10
Farook, T. H., Haq, T. M., Ramees, L., et al. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03047-6 · Cited by 9
“The study adhered to the minimum information for the clinical artificial intelligence modelling (MI-CLAIM) checklist [26].”
MethodBoyd, C., Brown, G. C., Kleinig, T. J., Mayer, W., Dawson, J. A., Jenkinson, M., & Bezak, E. (2024) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.14542 · Cited by 10
“20,21 In this work, a process is developed for guiding medical physicists to investigate hyperparameters and evaluate the reliability and stability of a chosen hyper-parameter set.”
Evidence-Based Artificial Intelligence in Medical Imaging
Streiner, D. L., Saboury, B., & Zukotynski, K. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.09.005 · Cited by 9
Quality assessment of stroke radiomics studies: Promoting clinical application
Sohn, B., & Won, S. Y. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110752 · Cited by 9
Torres-Silva, E. A., Rúa, S., Giraldo-Forero, A. F., Durango, M. C., Florez-Arango, J. F., & Orozco‐Duque, A. (2023) · Applied Sciences · doi:10.3390/app131910725 · Cited by 9
GPT for RCTs?: Using AI to determine adherence to reporting guidelines
Wrightson, J. G., Blazey, P., Moher, D., Khan, K. M., & Ardern, C. L. (2023) · doi:10.1101/2023.12.14.23299971 · Cited by 24
Askar, M., Tafavvoghi, M., Småbrekke, L., Bongo, L. A., & Svendsen, K. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309175 · Cited by 9
“Reporting model developing codes and performed experiments can help understand the final methodology, accelerate the overall development, and ensure that models are safeguarded from data leakage and other downfalls in model development [71,111].”
BackgroundMethodEthical, legal, and social issues (ELSI) and reporting guidelines of AI research in healthcare
Kameyama, J., Kodera, S., & Inoue, Y. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000607 · Cited by 8
de Mattos, B. P., Mattjie, C., Ravazio, R., Barros, R. C., & Grassi‐Oliveira, R. (2024) · International Journal of Mental Health and Addiction · doi:10.1007/s11469-024-01403-z · Cited by 9
“…and risk of bias was conducted independently by the three main reviewers at both the study and outcome levels, using two distinct instruments recommended by the EQUATOR network (EQUATOR Network, 2024): the MI-CLAIM checklist (Norgeot et al., 2020) and the CHARMS checklist (Moons et al., 2014).”
de Lima, R. C., & Quaresma, J. A. S. (2025) · Computers · doi:10.3390/computers14050202 · Cited by 11
Zhang, X., Ferry, J., Hewson, D. W., Collins, P. G. S., Wiles, M. D., Zhao, Y., Martindale, A. P. L., Tomaschek, M., Bowness, J. S., & the GRAITE‐USRA Working Group (2025) · Anaesthesia · doi:10.1111/anae.16733 · Cited by 10
Liang, Y. G., Fan, L., Teixeira-Pinto, A. M., Liew, G., & White, A. J. R. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02321-7 · Cited by 12
A framework of digital biomarkers for neurodegenerative diseases
Nerrise, F., Schütz, N., Zhao, Q., Gould, C. E., Milstein, A. S., Schulman, K. A., Henderson, V. W., Landay, J. A., Fei-Fei, L., Lin, F. V., et al. (2026) · Nature Reviews Bioengineering · doi:10.1038/s44222-026-00433-7 · Cited by 8
“…Interventional Trials (SPIRIT-AI) reporting guidelines 158 , the Minimum Information about Clinical Artificial Intelligence Modelling (MI-CLAIM) checklist 159 , and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD+AI) checklist 160,161 .”
ChatGPT/AI in Healthcare Management
Benet, D. (2023) · Journal of Clinical Medical Research · doi:10.46889/jcmr.2023.4301 · Cited by 8
The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review
Zrubka, Z., Kertész, G., Gulàcsi, L., Czere, J. T., Hölgyesi, Á., Motahari-Nezhad, H., Mosavi, A. H., Kovács, L., Butte, A. J., & Péntek, M. (2023) · Journal of Medical Internet Research · doi:10.2196/47430 · Cited by 10
“…of ML in pediatric DM, we applied the Minimum Information About Clinical Artificial Intelligence Modelling (MI-CLAIM) checklist, a general-purpose reporting guideline for medical AI studies available from the EQUATOR (Enhancing the Quality and Transparency of Health Research) network [64].”
InfluentialSelf-citationDerivation and Validation of a Machine Learning Model for the Prevention of Unplanned Dialysis
Klamrowski, M. M., Klein, R., McCudden, C. R., Green, J. R., Rashidi, B., White, C. A., Oliver, M. J., Molnar, A. O., Edwards, C., Ramsay, T., et al. (2024) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.0000000000000489 · Cited by 8
“16”
Evangelista, E. G., & Bensoussan, Y. E. (2024) · Otolaryngologic Clinics of North America · doi:10.1016/j.otc.2024.04.005 · Cited by 8
Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care
Aminorroaya, A., Biswas, D., Pedroso, A. F., & Khera, R. (2025) · Journal of the Society for Cardiovascular Angiography & Interventions · doi:10.1016/j.jscai.2025.102562 · Cited by 10
Can Machine Learning Help Simplify the Measurement of Diastolic Function in Echocardiography?
Arnaout, R. (2021) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2021.06.007 · Cited by 9
“…for just this, re imagining diastolic functional assessment on echo as a task a computer can accomplish with established segmentation and time-series classification methods, overall presenting the work in accordance with emerging guidance for scientific rigor in medical machine learning(9–11).”
MethodSelf-citation
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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