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
“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]
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
“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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Self-citationScalable and accurate deep learning with electronic health records.
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“…evaluation of predictive model capabilities by considering various metrics such as accuracy, sensitivity, precision, specificity, F 1 -score, decision curves, and so on [151], which are also required to be reported in ML prediction studies as advocated by several reporting guidelines [32-34].”
Uwimana, A., Gnecco, G., & Riccaboni, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109391 · Cited by 51
Baumert, M., Cowie, M., Redline, S. S., Mehra, R., Arzt, M. E., Pépin, J., & Linz, D. (2022) · SLEEP · doi:10.1093/sleep/zsac183 · Cited by 42
“…most algorithms are not described in detail in the literature; the majority of data reported by these wearables are computed by artificial intelligence algorithms with a “black box” effect [4,5], and lack of comprehensive validation studies are limiting wider acceptance by patients and clinicians.”
BackgroundArtificial intelligence for the next generation of precision oncology
Ballester, P. J., & Carmona, J. (2021) · npj Precision Oncology · doi:10.1038/s41698-021-00216-w · Cited by 51
“Many of these issues have been approached by recent checklists intending to promote a standard and transparent reporting of clinical interventions involving ML approaches 29 – 31 .”
BackgroundLiu, J., Zhang, Y., Wang, K., Yavuz, M. C., Chen, X., Yuan, Y., Li, H., Yang, Y., Yuille, A., Tang, Y., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103226 · Cited by 48
“The generalizability of medical AI models is a crucial expectation, as they should perform well on new data from various hospitals, rather than being tailored to a single dataset (Mongan et al., 2020; Norgeot et al., 2020; Guo et al., 2021b).”
BackgroundCzap, A. L., Bahr-Hosseini, M., Singh, N., et al. (2021) · Stroke · doi:10.1161/strokeaha.121.036091 · Cited by 43
Current state and prospects of artificial intelligence in allergy
van Breugel, M., Fehrmann, R. S. N., Bügel, M., Rezwan, F. I., Holloway, J. W., Nawijn, M. C., Fontanella, S., Čustović, A., & Koppelman, G. H. (2023) · Allergy · doi:10.1111/all.15849 · Cited by 46
Gill, S. K., Barsky, A., Guan, X., Bunting, K. V., Karwath, A., Țica, O., Stanbury, M., Haynes, S., Folarin, A., Dobson, R., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03094-4 · Cited by 49
AI in medical physics: guidelines for publication
Naqa, I. E., Boone, J. M., Benedict, S., Goodsitt, M. M., Chan, H., Drukker, K., Hadjiiski, L. M., Ruan, D., & Sahiner, B. (2021) · Medical Physics · doi:10.1002/mp.15170 · Cited by 43
Big Data in Laboratory Medicine—FAIR Quality for AI?
Blatter, T. U., Witte, H., Nakas, C. T., & Leichtle, A. B. (2022) · Diagnostics · doi:10.3390/diagnostics12081923 · Cited by 43
“Here, Minimum Information Checklists (MICs), stating the minimum requirements for quality and quantity to make data descriptions accurate and useful, could offer a needed standardization to track data quality from various sources [48,49].”
BackgroundVodenčarević, A., Weingärtner, M., Jaime, J., et al. (2022) · Stroke · doi:10.1161/strokeaha.121.036557 · Cited by 42
Tedeschi, L. O. (2022) · Journal of Animal Science · doi:10.1093/jas/skac111 · Cited by 45
Prijs, J., Liao, Z., To, M., Verjans, J. W. H., Jutte, P. C., Stirler, V. M. A., Olczak, J., Gordon, M., Guss, D., DiGiovanni, C. W., et al. (2022) · European Journal of Trauma and Emergency Surgery · doi:10.1007/s00068-022-02136-1 · Cited by 45
“This study was conducted according to the Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research [27] as well as the CONSORT-AI [28] the SPIRIT-AI [29], MI-CLAIM [30], and the CAIR checklist [31].”
MethodPrediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging
Wouters, A., Robben, D., Christensen, S., et al. (2021) · Stroke · doi:10.1161/strokeaha.121.034444 · Cited by 42
Tareq, A., Faisal, M. I., Islam, M. S., et al. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20075351 · Cited by 41
“The current study design adhered to Nature Medicine’s minimum information for clinical artificial intelligence modeling (MI-CLAIM) protocol [16].”
MethodArtificial intelligence for MRI stroke detection: a systematic review and meta-analysis
Bojsen, J. A., Elhakim, M. T., Graumann, O., Gaist, D., Nielsen, M., Harbo, F. S. G., Krag, C. H., Sagar, M. V., Kruuse, C. R., Boesen, M., et al. (2024) · Insights into Imaging · doi:10.1186/s13244-024-01723-7 · Cited by 48
“The MI-CLAIM checklists [21] were quantitatively synthesised for each study to identify trends in insuf fi ciency in reporting.”
BackgroundMethodInfluentialRíos, R., Miller, R. J. H., Manral, N., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105449 · Cited by 46
Considerations in the reliability and fairness audits of predictive models for advance care planning
Lu, J., Sattler, A. L., Wang, S. X. Y., Khaki, A. R., Callahan, A., Fleming, S. L., Fong, R., Ehlert, B., Li, R., Shieh, L. Y., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.943768 · Cited by 44
“Some commonly included items related to fairness include summary statistics (10, 11, 15, 17, 18, 20), like the distribution of demographics such as sex (11, 15, 17, 20) and race/ethnicity (15, 17, 20), as well as subgroup analyses that investigate how a model performs for specific subpopulations (7, 9, 11–13, 15, 18,…”
BackgroundArtificial intelligence tool development: what clinicians need to know?
Chew, B.-H., & Ngiam, K. Y. (2025) · BMC Medicine · doi:10.1186/s12916-025-04076-0 · Cited by 41
“Its performance should be evaluated and compared with that of the baseline model or standard of care before external validation, especially if the model is applied in different settings from where it was developed and then deployed in practice [37].”
Leandrou, S., Lamnisos, D., Bougias, H., Stogiannos, N., Georgiadou, E., Achilleos, K. G., Pattichis, C. S., & Initiative, A. D. N. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1149871 · Cited by 42
“To engage in a transparent way of reporting AI-based studies, this article is also aligned with the Minimum Information about CLinical Artificial Intelligence Modeling (MI-CLAIM) checklist (Norgeot et al., 2020).”
MethodWilliams, M. C., Weir-McCall, J. R., Baldassarre, L. A., Cecco, C. N. D., Choi, A. D., Dey, D., Dweck, M. R., Išgum, I., Kolossváry, M., Leipsic, J. A., et al. (2024) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2024.08.003 · Cited by 38
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Ramlakhan, S. L., Saatchi, R., Sabir, L., Singh, Y., Hughes, R., Shobayo, O., & Ventour, D. (2022) · Emergency Medicine Journal · doi:10.1136/emermed-2021-212068 · Cited by 40
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“SPIRIT-AI (Cruz Rivera et al. 2020), CONSORT-AI (Liu et al. 2020b) and MI-CLAIM (Norgeot et al. 2020) are guidelines concerning the use of AI in clinical trials and state which aspects need to be considered to improve trans-parency, interpretability and reportability before starting.”
MethodAdvances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction
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Schallmoser, S., Züger, T., Kraus, M., Saar‐Tsechansky, M., Stettler, C., & Feuerriegel, S. (2023) · Journal of Medical Internet Research · doi:10.2196/42181 · Cited by 35
“We report the Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist [36], which was developed to improve transparent reporting of ML in medicine, in Multimedia Appendix 8 [36].”
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“Cross-validation is the standard approach for the validation of machine learning models [33].”
MethodHaymond, S., & Master, S. R. (2021) · Clinical Chemistry · doi:10.1093/clinchem/hvab272 · Cited by 33
Almăşan, O. C., Leucuta, D., Hedesiu, M. C., Mureșanu, S., & Popa, S. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12030942 · Cited by 40
“Zhang W. [16] used the same subjects as Bianchi J. [34] but used Learning using Privileged Information (LUPI) on 77 features (6 clinical, 46 imaging, and 25 protein) and interactions, finding that the LUPI method outperformed non-LUPI methods.”
InfluentialApplications of Artificial Intelligence and Deep Learning in Glaucoma
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“151 None of the studies reviewed speci fi cally addressed items recommended in these reporting guidelines from preclinical to clinical stages.”
BackgroundUsing preregistration as a tool for transparent fNIRS study design
Schroeder, P. A., Artemenko, C., Kosie, J. E., Cockx, H., Stute, K., Pereira, J., Klein, F., & Mehler, D. M. A. (2023) · Neurophotonics · doi:10.1117/1.nph.10.2.023515 · Cited by 36
The MI-CLAIM-GEN checklist for generative artificial intelligence in health
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Self-citationArtificial Intelligence in Prosthodontics: Transforming Diagnosis and Treatment Planning
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Miao, B. Y., Arneson, D. V., Wang, M., & Butte, A. J. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000008 · Cited by 34
“Methods to recognize and remove such biases are paramount to ensuring the delivery of equitable healthcare, and device labeling requirements that clarify how these models were trained, or how they derived their outputs, may help promote transparency in AI/ML-based DTx [15].”
BackgroundSelf-citationSelf-supervised learning for label-free segmentation in cardiac ultrasound
Ferreira, D. L., Lau, C., Salaymang, Z., & Arnaout, R. (2025) · Nature Communications · doi:10.1038/s41467-025-59451-5 · Cited by 38
Self-citationLearning Causal Effects From Observational Data in Healthcare: A Review and Summary
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“(109) was proposed regarding the study design of projects, preparation and usage of data, model selection, performance evaluation, model validation, and data pipelines.”
BackgroundSelf-citationDomain-guided data augmentation for deep learning on medical imaging
Athalye, C., & Arnaout, R. (2023) · PLoS ONE · doi:10.1371/journal.pone.0282532 · Cited by 36
Self-citationGraham, S., Minhas, F., Bilal, M., Ali, M., Tsang, Y. W., Eastwood, M., Wahab, N., Jahanifar, M., Hero, E., Dodd, K., et al. (2023) · Gut · doi:10.1136/gutjnl-2023-329512 · Cited by 33
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Barberis, A., Aerts, H. J. W. L., & Buffa, F. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-51381-4 · Cited by 33
Large language model-driven sentiment analysis for facilitating fibromyalgia diagnosis
Venerito, V., & Iannone, F. (2024) · RMD Open · doi:10.1136/rmdopen-2024-004367 · Cited by 30
Hetz, M. J., Carl, N., Haggenmüller, S., Wies, C., Kather, J. N., Michel, M., Wessels, F., & Brinker, T. J. (2024) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2024.100078 · Cited by 33
Lessons learned in transitioning to AI in the medical imaging of COVID-19
Naqa, I. M. E., Hu, Q., Chen, W., Li, H., Fuhrman, J. D., Gorre, N., & Giger, M. L. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.s1.010902 · Cited by 30
Silverman, A. L., Shung, D. L., Stidham, R. W., Kochhar, G. S., & Iacucci, M. (2024) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2024.05.048 · Cited by 30
Recommendations for Successful Development and Implementation of Digital Health Technology Tools
Loo, R. T. J., Nasta, F., Macchi, M., Baudot, A., Burstein, F., Bove, R. M., Greve, M., Fröhlich, H., Khalid, S., Küderle, A., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/56747 · Cited by 28
“Clinical quality standards and common data elements [33,34] can also guide multimodal integration, while standardized software pipelines [35] and reference datasets support robust data processing.”
Deep learning for emergency ascites diagnosis using ultrasonography images
Lin, Z., Li, Z., Cao, P., Lin, Y., Liang, F., He, J., & Huang, L. (2022) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.13695 · Cited by 30
Hasan, H. A., Saad, F. H., Ahmed, S., et al. (2023) · Oral Radiology · doi:10.1007/s11282-023-00685-8 · Cited by 27
Coombs, L., Orlando, A., Wang, X., Shaw, P., Rich, A. S., Lakhtakia, S., Titchener, K., Adamson, B., Miksad, R. A., & Mooney, K. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00660-3 · Cited by 27
Nicolaes, J., Tselenti, E., Aouad, T., Medina, C. L., Feydy, A., Talbot, H., Hoepken, B., de Peyrecave, N., & Dougados, M. (2024) · Annals of the Rheumatic Diseases · doi:10.1136/ard-2024-225862 · Cited by 29
“21 24 Here, we further evaluate the potential of this trained deep-learning algorithm in a larger external validation cohort, including patients with both radiographic axSpA (r-axSpA) and nr-axSpA, to assess its potential for clinical use.”
Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies
Dwivedi, R., Yan, T., Park, B., Wei, M., Horgan, K., Madigan, D., & Yu, B. (2020) · International Statistical Review · doi:10.1111/insr.12427 · Cited by 31
“Recent voices that urge the community to give external validation a higher priority across many domains (Debray et al., 2015; Krauss, 2018; Norgeot et al., 2020) are very much in accordance with Yu and Kumbier's (2020) call to statisticians to broaden the scope of their concern from data modelling…”
Self-citationSalahin, S. M. S., Ullaa, M. D. S., Ahmed, S., Mohammed, N., Farook, T. H., & Dudley, J. (2023) · Oral · doi:10.3390/oral3020016 · Cited by 28
“Oral 2023, 3, FOR PEER REVIEW 7 The MI-CLAIM model dictates that all definitions of the evaluation metrics be stated.”
MethodAI Quality Standards in Health Care: Rapid Umbrella Review
Kuziemsky, C. E., Chrimes, D., Minshall, S. R., Mannerow, M., & Lau, F. (2024) · Journal of Medical Internet Research · doi:10.2196/54705 · Cited by 30
“Beyond the EQUATOR guidelines, there are also the Minimum Information for Medical AI Reporting standard [18] and the Minimum Information About Clinical Artificial Intelligence Modeling checklist [19] on the minimum information needed in published AI studies.”
MethodKempf, E., Priou, S., Lamé, G., Daniel, C., Bellamine, A., Sommacale, D., Belkacémi, Y., Bey, R., Galula, G., Taright, N., et al. (2022) · International Journal of Cancer · doi:10.1002/ijc.33928 · Cited by 28
The Future of Orthodontics: Deep Learning Technologies
Surendran, A., Daigavane, P., Shrivastav, S., Kamble, R. H., Sanchla, A. D., Bharti, L., & Shinde, M. (2024) · Cureus · doi:10.7759/cureus.62045 · Cited by 29
“The minimum-information-about-clinical-artificial-intelligence modeling (MI-CLAIM) framework, proposed by Norgeot et al., seeks to resolve this by establishing uniform standards for effectiveness and transparency in clinical AI modeling [81].”
MethodGPT for RCTs? Using AI to determine adherence to clinical trial reporting guidelines
Wrightson, J. G., Blazey, P., Moher, D., Khan, K. M., & Ardern, C. L. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-088735 · Cited by 29
Huang, Q., Wu, S., Ou, Z., & Gao, Y. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.03.004 · Cited by 29
Helman, S. M., Herrup, E. A., Christopher, A. B., & Al‐Zaiti, S. S. (2021) · Cardiology in the Young · doi:10.1017/s1047951121004212 · Cited by 25
“18 Simple descriptive statistics and pie charts were used to report frequencies of different machine learning algorithms applied across eligible studies.”
MethodCarolan, J. E., McGonigle, J., Dennis, A., Lorgelly, P. K., & Banerjee, A. (2022) · JMIR Medical Informatics · doi:10.2196/34038 · Cited by 24
“Diagnosis-Artificial Intelligence (TRIPOD-AI), and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) [9,10].”
MethodLysdahlgaard, S. (2023) · Radiography · doi:10.1016/j.radi.2023.09.012 · Cited by 24
Verma, N., Duseja, A. K., Mehta, M., De, A., Lin, H., Wong, V. W., Wong, G. L., Rajaram, R. B., Chan, W., Mahadeva, S., et al. (2024) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.17891 · Cited by 24
“As supported by MI-CLAIM guidelines, 17 at the outset we separated clinical data of patients from India as validation set for several reasons, viz. to ensure geographical generalisation of models, real world applicability, avoiding overfitting to local patterns, ensuring diversity, mimicking future…”
MethodEken, A., Nassehi, F., & Eroğul, O. (2024) · Reviews in the Neurosciences · doi:10.1515/revneuro-2023-0117 · Cited by 25
O’Shea, R. J., Sharkey, A. R., Cook, G., & Goh, V. (2021) · European Radiology · doi:10.1007/s00330-021-07881-2 · Cited by 28
“AIfocused guidelines have recently been developed including CLAIM [18], SPIRIT-AI [19], MI-CLAIM [20] and, prospectively, STARD-AI [21].”
MethodNielsen, M., Waldmann, M., Frölich, A. M., et al. (2021) · Stroke · doi:10.1161/strokeaha.120.033807 · Cited by 23
Atkins, D., Makridis, C. A., Alterovitz, G., Ramoni, R. B., & Clancy, C. M. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122220-110053 · Cited by 24
Graham, S. S. (2022) · doi:10.1093/oso/9780197644461.001.0001 · Cited by 25
Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress
Martins, M. V., Baptista, L., Luís, H., Assunção, V. A., Araújo, M., & Realinho, V. (2023) · Computation · doi:10.3390/computation11060115 · Cited by 24
“In the meantime, there are checklists available that can and should serve as guidance for researchers and reviewers [57,58].”
BackgroundArtificial Intelligence for Risk Assessment on Primary Prevention of Coronary Artery Disease
Chen, S., Loguercio, S., Chen, K., Lee, S. E., Park, J., Liu, S., Sadaei, H. J., & Torkamani, A. (2023) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-023-00731-4 · Cited by 26
“A coordinated effort is needed to address these challenges and to ensure that AI is used ethically and responsibly in healthcare management [269, 270].”
BackgroundSelf-citationvan Dijk, D. P. J., Volmer, L., Brecheisen, R., Martens, B., Dolan, R. D., Bryce, A. S., Chang, D. K.-F., McMillan, D. C., Stoot, J. H. M. B., West, M. A., et al. (2024) · British Journal of Radiology · doi:10.1093/bjr/tqae191 · Cited by 31
“Healthcare artificial intelligence (AI) guidelines in general, 14,15 and, specifically, the “Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis” (TRIPOD) guidelines, 16 recommend that a fully independent external validation—with entirely different…”
Artificial intelligence-aided data mining of medical records for cancer detection and screening
Haue, A. D., Hjaltelin, J. X., Holm, P. C., Placido, D., & Brunak, S. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00277-8 · Cited by 26
Quantum deep learning in neuroinformatics: a systematic review
Orka, N. A., Awal, M. A., Lió, P., Pogrebna, G., Ross, A. G., & Moni, M. A. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11136-7 · Cited by 27
“Using the MI-CLAIM checklist (Norgeot 2020 ), we thoroughly assessed the risk of bias for all the included studies (refer to Supplementary Material 2, 3).”
Reproducibility of prediction models in health services research
Belbasis, L., & Panagiotou, O. A. (2022) · BMC Research Notes · doi:10.1186/s13104-022-06082-4 · Cited by 23
“The MI-CLAIM checklist was developed to improve transparent reporting of machine learning algorithms in medicine, and it has similarities with TRIPOD statement [46].”
MethodMachine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes
Züger, T., Schallmoser, S., Kraus, M., Saar‐Tsechansky, M., Feuerriegel, S., & Stettler, C. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0210 · Cited by 22
“We report the MI-CLAIM checklist, 12 which was developed to improve transparent reporting of ML in medicine, in Supplementary Data S8 (Table S3).”
Early Identification of Candidates for Epilepsy Surgery
Wissel, B. D., Greiner, H. M., Glauser, T. A., Pestian, J. P., Ficker, D. M., Cavitt, J. L., Estofan, L., Holland‐Bouley, K. D., Mangano, F. T., Szczesniak, R. D., et al. (2024) · Neurology · doi:10.1212/wnl.0000000000208048 · Cited by 22
Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review
Pal, P., Pooja, K., Nabi, Z., Gupta, R., Tandan, M., Rao, G. V., & Reddy, D. N. (2024) · Indian Journal of Gastroenterology · doi:10.1007/s12664-024-01531-3 · Cited by 23
“Finally, we extracted information based on the MI-CLAIM (minimum information about clinical artificial intelligence modeling) checklist [5].”
Artificial Intelligence in Cardiology: An Australian Perspective
Jaltotage, B., Ihdayhid, A. R., Lan, N. S. R., Pathan, F. K., Patel, S. V., Arnott, C. G., Figtree, G. A., Kritharides, L., Islam, S. M. S., Chow, C. K., et al. (2023) · Heart Lung and Circulation · doi:10.1016/j.hlc.2023.06.703 · Cited by 21
Transparency in Artificial Intelligence Reporting in Ophthalmology-A Scoping Review
Chen, D. K., Geevarghese, A., Lee, S., Plovnick, C., Elgin, C. Y., Zhou, R. W., Oermann, E. K., Aphinyonaphongs, Y., & Al‐Aswad, L. A. (2024) · Ophthalmology Science · doi:10.1016/j.xops.2024.100471 · Cited by 21
The TRIPOD-LLM Statement: A Targeted Guideline For Reporting Large Language Models UsePreprint
Gallifant, J., Afshar, M., Ameen, S., Aphinyanaphongs, Y., Chen, S., Cacciamani, G. E., Demner‐Fushman, D., Dligach, D., Daneshjou, R., Fernandes, C. O., et al. (2024) · medRxiv · doi:10.1101/2024.07.24.24310930 · Cited by 21
Collins, G. S., Whittle, R., Bullock, G. S., Logullo, P., Dhiman, P., de Beyer, J. A., Riley, R. D., & Schlussel, M. M. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2023.10.015 · Cited by 21
Lorde, N., Mahapatra, S., & Kalaria, T. (2024) · Diagnostics · doi:10.3390/diagnostics14161808 · Cited by 21
Hergès, H. O., Vithal, R., El‐Merhi, A., et al. (2021) · Acta Neurologica Scandinavica · doi:10.1111/ane.13541 · Cited by 20
Cheema, B. S., Mutharasan, R. K., Sharma, A. K., Jacobs, M. L., Powers, K., Lehrer, S., Wehbe, F., Ronald, J., Pifer, L., Rich, J. D., et al. (2022) · JACC Advances · doi:10.1016/j.jacadv.2022.100123 · Cited by 20
Munguía‐Realpozo, P., Etchegaray‐Morales, I., Pinto, C. M., Méndez‐Martínez, S., Osorio-Peña, Á. D., Ayón-Aguilar, J., & García‐Carrasco, M. (2023) · Autoimmunity Reviews · doi:10.1016/j.autrev.2023.103294 · Cited by 23
Tovar, D., Rosenthal, M. H., Maitra, A., & Koay, E. J. (2023) · Artificial Intelligence Surgery · doi:10.20517/ais.2022.38 · Cited by 22
“To implement the AI in the clinical setting, the researcher’s ultimate goal, the code, computer requirements, notes, or any factors needed for the model building are provided or externally evaluated for reproducibility and accuracy [77] .”
BackgroundMethodGonzález, R., Saha, A., Campbell, C. J. V., Nejat, P., Lokker, C., & Norgan, A. P. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100347 · Cited by 24
Machine Learning in Emergency Medicine: Keys to Future Success
Taylor, R. A., & Haimovich, A. D. (2020) · Academic Emergency Medicine · doi:10.1111/acem.14189 · Cited by 20
“Model clinical utility is also dependent on benchmarks that can be applied in the interpretation at the level of individual patients, namely, positive/negative predictive values, numbers needed to treat/harm, sensitivity, and specificity.(6)”
BackgroundMethodInfluentialArtificial intelligence reporting guidelines: what the pediatric radiologist needs to know
Meshaka, R., dos Santos, D. P., Arthurs, O. J., Sebire, N. J., & Shelmerdine, S. C. (2021) · Pediatric Radiology · doi:10.1007/s00247-021-05129-1 · Cited by 18
“These are not to be confused with MI-CLAIM [17] (Minimum Information about Clinical Artificial Intelligence Modeling), which is not intended for health care professionals.”
BackgroundMachine Learning to Predict Fascial Dehiscence after Exploratory Laparotomy Surgery
Cole, J., Hughey, S. B., Metzger, A., Geiger, P., Fluke, L. M., & Booth, G. J. (2021) · Journal of Surgical Research · doi:10.1016/j.jss.2021.06.068 · Cited by 20
Verma, N., Choudhury, A. K., Singh, V., Duseja, A. K., Al‐Mahtab, M., Devarbhavi, H. C., Eapen, C. E., Goel, A., Ning, Q., Duan, Z., et al. (2022) · Liver International · doi:10.1111/liv.15361 · Cited by 19
Artificial Intelligence for Outcome Modeling in Radiotherapy
Cui, S., Hope, A. J., Dilling, T. J., Dawson, L. A., Haken, R. K. T., & Naqa, I. E. (2022) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2022.06.005 · Cited by 19
The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review
Vrdoljak, J., Krešo, A., Kumrić, M., Martinović, D., Cvitković, I., Grahovac, M., Vickov, J., Bukić, J., & Božić, J. (2023) · Cancers · doi:10.3390/cancers15082400 · Cited by 22
“Several guidelines that tackle reporting in medical prediction studies have been previously published [56–59].”
Deep learning evaluation of echocardiograms to identify occult atrial fibrillation
Yuan, N., Stein, N. R., Duffy, G., Sandhu, R. K., Chugh, S. S., Chen, P., Rosenberg, C., Albert, C. M., Cheng, S. C., Siegel, R. J., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01090-z · Cited by 22
Perspectives: A surgeon’s guide to machine learning
Kuo, R. Y. L., Harrison, C. J., Jones, B., Geoghegan, L., & Furniss, D. (2021) · International Journal of Surgery · doi:10.1016/j.ijsu.2021.106133 · Cited by 19
King, A., Fowler, G., Macefield, R. C., Walker, H., Thomas, C., Markar, S. R., Higgins, E., Blazeby, J. M., & Blencowe, N. (2025) · BJS Open · doi:10.1093/bjsopen/zraf073 · Cited by 22
Dwyer, B., Flathers, M., Sano, A., Dempsey, A. G., Cipriani, A., Gazi, A. H., Hill, B., Gorban, C., Rodríguez, C. I., Stromeyer, C., et al. (2025) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-025-00049-6 · Cited by 31
Artificial intelligence applications in brachytherapy: A literature review
Zhao, J., Ni, R., Chow, R., Rink, A., Weersink, R., Croke, J. M., & Raman, S. (2023) · Brachytherapy · doi:10.1016/j.brachy.2023.04.003 · Cited by 18
The Evolution and Reliability of Machine Learning Techniques for Oncology
Owida, H. A., Moh’d, B. A.-H., Turab, N. M., Al-Nabulsi, J., & Abuowaida, S. F. A. (2023) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v19i08.39433 · Cited by 24
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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