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.
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Claims the paper makes
3 of 4 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured
- Premisecitation
The same methods that enable new analyses of clinical data can also allow patient re-identification in ways that are sometimes unpredictable.
“Furthermore, the same methods that are enabling new analyses of clinical data may also enable the re-identification of patients in sometimes unpredictable ways17.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [17] Schwarz, C. G. et al. N. Engl. J. Med. 381, 1684–1686 (2019). source
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)
Experimental — accuracy not yet measured
- Interpretationcitation§ Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
Newer machine-learning approaches carry a risk of greater complexity and reduced interpretability, even when advanced interpretation methods are applied.
“Newer methodologies have the danger of becoming more complex and less interpretable, even when sophisticated interpretation technique are used11.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [11] Lipton, Z. C. Queue 16, 31–57 (2018). source
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)
Experimental — accuracy not yet measured
- Premisecitation§ Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
Minimum information guidelines have substantially improved the downstream utility, transparency, and interpretability of data in repositories and publications across many other research domains.
“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.”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [15] Moher, D. et al. Br. Med. J. 340, c869 (2010). source
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)
Experimental — accuracy not yet measured - cites [16] von Elm, E. et al. Ann. Intern. Med. 147, 573–577 (2007). source
- Interpretationreasoning§ Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
Inadequate documentation of how training and test cohorts were selected, how models were developed, and how they were validated has contributed to confusion in the field.
“Unclear documentation on training and test-cohort selection, development methodology, and how systems were validated has added to the confusion.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 2
Classified as probably not a substantial claim.
Premisenone givenReal-world circumstances can prevent complete sharing of code, for example when commercial entities treat their codebase as a proprietary trade secret.
“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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenNo single method for examining or interpreting a model is perfect.
“Furthermore, no single examination or interpretation methodology is perfect.”
Quote in the paperNot a substantial claim
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 subsequent evidence review underscored the limits of current knowledge and research about health care algorithms in the literature.
“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]
It has been widely recommended to report any measures taken to prevent overfitting.
“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]
When possible, the literature recommends comparing current best practice to AIPM performance.
“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]
Code for the complete model building pipeline should be published in well-documented scripts with computer environment requirements when possible.
“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]
Performance analysis by population subgroups or specific problematic use cases is recommended to identify algorithmic bias.
“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]
The literature recommends assessing generalizability on external data from a different time period, place, or healthcare setting.
“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]
To ensure generalizability to the intended healthcare setting, developers are advised to extensively validate the model for representative data from that setting.
“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]
Medical AI models should not only have high performance, but should also be interpretable.
“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]
The MI-CLAIM checklist is available as Supplementary Table 7.
“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]
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“In addition, the minimum information about clinical artificial intelligence modeling (MI-CLAIM) criterion provides considerations specific for AI.(50) MI-CLAIM emphasizes the need to perform benchmarking, clearly define input data types, and describe preprocessing data steps (e.”
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“For instance, a multi-institutional group has recently compiled a document establishing guidelines on Minimum information about clinical artificial intelligence modeling by means of the MI-CLAIM checklist (252).”
MethodApplication of Deep Learning in Breast Cancer Imaging
Balkenende, L., Teuwen, J., & Mann, R. M. (2022) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2022.02.003 · Cited by 174 · see citing works
Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis
Park, S. H., Han, K., Jang, H. Y., Park, J. E., Lee, J.-G., Kim, D. W., & Choi, J. (2023) · Radiology · doi:10.1148/radiol.220182 · Cited by 163 · see citing works
Where Medical Statistics Meets Artificial Intelligence
Hunter, D. J., & Holmes, C. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2212850 · Cited by 165 · see citing works
Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis
Kolbinger, F. R., Veldhuizen, G. P., Zhu, J., Truhn, D., & Kather, J. N. (2024) · Communications Medicine · doi:10.1038/s43856-024-00492-0 · Cited by 160 · see citing works
From real-world electronic health record data to real-world results using artificial intelligence
Knevel, R., & Liao, K. P. (2022) · Annals of the Rheumatic Diseases · doi:10.1136/ard-2022-222626 · Cited by 163 · see citing works
“44 In addition, methods are being developed that allow a more automated approach for determining the equivalent codes across EHR systems to use in a model.”
MethodDeep learning-aided decision support for diagnosis of skin disease across skin tones
Groh, M., Badri, O., Daneshjou, R., Koochek, A., Harris, C., Soenksen, L. R., Doraiswamy, P. M., & Picard, R. (2024) · Nature Medicine · doi:10.1038/s41591-023-02728-3 · Cited by 160 · see citing works
APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support
Kwong, J. C. C., Khondker, A., Lajkosz, K., McDermott, M. B. A., Borrat, X., McCradden, M. D., Mamdani, M., Kulkarni, G. S., & Johnson, A. E. W. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.35377 · Cited by 151 · see citing works
Navarro, C. L. A., Damen, J. A., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, P. G. S., Bajpai, R. C., Riley, R. D., Moons, K. G. M., et al. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01469-6 · Cited by 136 · see citing works
“Some reporting guidelines for ML clinical prediction models have already been developed [32, 33].”
BackgroundCai, Y., Cai, Y.-Q., Tang, L.-Y., Wang, Y.-H., Gong, M., Jing, T.-C., Li, H.-J., Li-Ling, J., Hu, W., Yin, Z., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03273-7 · Cited by 122 · see citing works
“…calculators that can allow batch processing of participant information (e.g., a prediction software or tool) ∙ APPRAISE‑AI [31] ∙ MI‑CLAIM [32] ∙ AI‑TREE [33] II Apply and report the classic algorithms that can be found in some common tools/platforms OR report complete codes and hyperpa‑…”
BackgroundApplication of Artificial Intelligence in Orthodontics: Current State and Future Perspectives
Liu, J., Zhang, C., & Shan, Z. (2023) · Healthcare · doi:10.3390/healthcare11202760 · Cited by 119 · see citing works
Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer
Wang, J., Yu, Y., Tan, Y., Wan, H., Zheng, N., He, Z., Mao, L., Ren, W., Chen, K., Lin, Z., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48705-3 · Cited by 119 · see citing works
“Our study adhered to the reporting and analysis guidelines of STARD, CONSORT-AI Extension, and the MI-CLAIM checklist 27 – 29 .”
Tsopra, R., Fernández, X. M., Luchinat, C., Alberghina, L., Lehrach, H. R., Vanoni, M., Dreher, F., Sezerman, O. U., Marc Cuggia, A., de Tayrac, M., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01634-3 · Cited by 117 · see citing works
Adversarial attacks and adversarial robustness in computational pathology
Laleh, N. G., Truhn, D., Veldhuizen, G. P., Han, T., van Treeck, M., Buelow, R. D., Langer, R., Dislich, B., Boor, P., Schulz, V., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33266-0 · Cited by 115 · see citing works
Dhiman, P., Ma, J., Navarro, C. L. A., Speich, B., Bullock, G. S., Damen, J. A., Kirtley, S., Hooft, L., Riley, R. D., CALSTER, B. V., et al. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.06.024 · Cited by 109 · see citing works
“…machine learning, we select papers where authors may not think TRIPOD is applicable, however, other machine learning reporting guidance is available, such as Minimum information about clinical artificial intelligence modelling (MI-CLAIM) checklist and reporting guideline by Luo et al [ 39 , 40 ].”
MethodArtificial Intelligence in Orthodontics: Critical Review
Nordblom, N. F., Büttner, M., & Schwendicke, F. (2024) · Journal of dentistry research · doi:10.1177/00220345241235606 · Cited by 118 · see citing works
“Researchers are encouraged to report model performances (e.g., successful prediction within a specific threshold, mean radial error) along with the performances of clinically used parameters and diagnoses in standardized formats (Norgeot et al. 2020).”
BackgroundHurkmans, C., Bibault, J.-E., Brock, K. K., van Elmpt, W., Feng, M., David Fuller, C., Jereczek-Fossa, B. A., Korreman, S., Landry, G., Madesta, F., et al. (2024) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2024.110345 · Cited by 110 · see citing works
Tornero-Costa, R., Martínez-Millana, A., Muscat, N. A., Lazëri, L., Traver, V., & Novillo-Ortiz, D. (2023) · JMIR Mental Health · doi:10.2196/42045 · Cited by 118 · see citing works
Machine learning and artificial intelligence in neuroscience: A primer for researchers
Badrulhisham, F., Pogatzki-Zahn, E. M., Segelcke, D., Spisák, T., & Vollert, J. (2023) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2023.11.005 · Cited by 109 · see citing works
“A framework for reporting applicable to all medical AI research can be found in the MI-CLAIM checklist (Norgeot et al., 2020).”
Foundational Considerations for Artificial Intelligence Using Ophthalmic Images
Abràmoff, M. D., Cunningham, B., Patel, B., Eydelman, M. B., Leng, T., Sakamoto, T., Blodi, B., Grenon, S. M., Wolf, R. M., Manrai, A. K., et al. (2022) · Ophthalmology · doi:10.1016/j.ophtha.2021.08.023 · Cited by 96 · see citing works
Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development
Bradshaw, T., Boellaard, R., Dutta, J., Jha, A. K., Jacobs, P. D., Li, Q., Liu, C., Sitek, A., Saboury, B., Scott, P. J. H., et al. (2021) · Journal of Nuclear Medicine · doi:10.2967/jnumed.121.262567 · Cited by 96 · see citing works
“This can be accomplished by using reporting checklists such as MI-CLAIM ( 29 ).”
MethodDhombres, F., Bonnard, J., Bailly, K., Maurice, P., Papageorghiou, A. T., & Jouannic, J.-M. (2022) · Journal of Medical Internet Research · doi:10.2196/35465 · Cited by 93 · see citing works
“Some medical publications have proposed glossaries and definitions of basic AI notions, and the first reference guidelines for reporting medical studies involving AI were published in 2020 [90-95].”
BackgroundAdamson, B. J. S., Waskom, M., Blarre, A., Kelly, J., Krismer, K., Nemeth, S., Gippetti, J., Ritten, J. J., Harrison, K., Ho, G., et al. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1180962 · Cited by 96 · see citing works
“When using ML and NLP for RWE, current guidance emphasizes transparency (NICE, 2022; Norgeot et al., 2020; Center for Drug Evaluation and Research Center for Biologics Evaluation and Research Oncology Center of Excellence; Padula et al., 2022; Blueprint for trustworthy AI implementation guidance and assurance for heal…”
Fraser, A. G., Biasin, E., Bijnens, B. H., Bruining, N., Caiani, E. G., Cobbaert, K., Davies, R. H., Gilbert, S., Hovestadt, L., Kamenjašević, E., et al. (2023) · Expert Review of Medical Devices · doi:10.1080/17434440.2023.2184685 · Cited by 103 · see citing works
“Methodological guidelines Park 2020 JAMIA Open [65] Cross-specialty Recommendations for a phased approach to medical AI evaluation Sendak 2020 EMJ Innovation [66] European Medical Journal Cross-specialty Recommendations for translation of ML products into healthcare delivery McCradden 2020 Nature Medicine [67] Cross-s…”
MethodStenzinger, A., Alber, M., Allgäuer, M., Jurmeister, P. S., Bockmayr, M. L., Budczies, J., Lennerz, J. K., Eschrich, J., Kazdal, D. N., Schirmacher, P., et al. (2021) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2021.02.011 · Cited by 91 · see citing works
Requirements and reliability of AI in the medical context
Balagurunathan, Y., Mitchell, R., & Naqa, I. E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.024 · Cited by 91 · see citing works
“In recent consensus statements that provide criteria to evaluate multivariate statistical methods have been well documented, below are a list of widely accepted contenders TRIPOD, CLAIM, MI-CLAIM [58–60], which were developed by a large survey of scholars and industrial experts’ opinions.”
BackgroundOverview of leakage scenarios in supervised machine learning
Sasse, L., Nicolaisen‐Sobesky, E., Dukart, J., Eickhoff, S. B., GOETZ, M. A., Hamdan, S., Komeyer, V., Kulkarni, A. J., Lahnakoski, J. M., Love, B. C., et al. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01193-8 · Cited by 118 · see citing works
“Further guidelines have been proposed for specialized domains such as biomedical applications [46] and minimum information about clinical artificial intelligence modeling [52].”
BackgroundReporting Use of AI in Research and Scholarly Publication—JAMA Network Guidance
Flanagin, A., Pirracchio, R., Khera, R., Berkwits, M., Hswen, Y., & Bibbins-Domingo, K. B. (2024) · JAMA · doi:10.1001/jama.2024.3471 · Cited by 96 · see citing works
Padula, W. V., Kreif, N., Vanness, D. J., Adamson, B. J. S., Rueda, J. D., Felizzi, F., Jonsson, P. F., IJzerman, M. J., Butte, A. J., & Crown, W. H. (2022) · Value in Health · doi:10.1016/j.jval.2022.03.022 · Cited by 89 · see citing works
Self-citationEnhancing trust in AI through industry self-governance.
Roski, J., Maier, E. J., Vigilante, K., Kane, E. A., & Matheny, M. E. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab065 · Cited by 83 · see citing works
“…& equitable population 33,39 • Lack of data management 37,40 • No accounting for causal pathways 41 • Data transparency & reporting 32,37,40,42–46 • Model provenance records 40 • Equitable/diverse workforce • Organizational implementation 38,46,47,56,57 • Organizational governance…”
BackgroundLadbury, C., Zarinshenas, R., Semwal, H., Tam, A., Vaidehi, N., Rodin, A. S., Liu, A., Glaser, S., Salgia, R., & Amini, A. (2022) · Translational Cancer Research · doi:10.21037/tcr-22-1626 · Cited by 84 · see citing works
“…model for individual prognosis or diagnosis (TRIPOD) (67), minimum information about clinical artificial intelligence modeling (MI-CLAIM) (68), or radiomics quality score (69), of course acknowledging that due to availability of suitable datasets opportunities for external validation may…”
Svennberg, E., Han, J. K., Caiani, E. G., Engelhardt, S., Ernst, S., Friedman, P. A., Garcia, R., Ghanbari, H., Hindricks, G., Man, S. H., et al. (2025) · EP Europace · doi:10.1093/europace/euaf071 · Cited by 92 · see citing works
Homeyer, A., Geißler, C., Schwen, L. O., Zakrzewski, F., Evans, T., Strohmenger, K., Westphal, M., Bülow, R. D., Kargl, M., Karjauv, A., et al. (2022) · Modern Pathology · doi:10.1038/s41379-022-01147-y · Cited by 83 · see citing works
“ically for machine learning approaches [118, 119] or such adaptation is under development [120–123].”
BackgroundTranslation of AI into oncology clinical practice
Naqa, I. E., Karolak, A. M., Luo, Y., Folio, L. R., Tarhini, A. A., Rollison, D. E. M., & Parodi, K. (2023) · Oncogene · doi:10.1038/s41388-023-02826-z · Cited by 83 · see citing works
“…for AI (TRIPOD-AI/ PROBAST-AI) [39], Checklist for AI in Medical Imaging (CLAIM) [40], Minimum information about clinical AI modeling (MI-CLAIM) [41], DEcision support systems driven by AI (DECIDE-AI) [42], checklist for AI in medical physics (CLAMP) [43], and Standard Protocol Items:…”
MethodDetecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models
Subasri, V., Krishnan, A., Kore, A., Dhalla, A., Pandya, D., Wang, B., Malkin, D., Razak, F., Verma, A. A., Goldenberg, A., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.13685 · Cited by 87 · see citing works
Bullock, G. S., Mylott, J. A., Hughes, T., Nicholson, K. F., Riley, R. D., & Collins, P. G. S. (2022) · Sports Medicine · doi:10.1007/s40279-022-01698-9 · Cited by 88 · see citing works
“However, there are machine learning reporting guide‐ lines available, such as the Minimum Information About Clinical Artificial Intelligence Modelling (MI‐CLAIM) checklist [82], which were not cited for any of the machine learning models, suggesting poor transparency and report‐ ing for these studies.”
Petersen, E., Potdevin, Y., Mohammadi, E., Zidowitz, S., Breyer, S., Nowotka, D., Henn, S., Pechmann, L., Leucker, M., Rostalski, P., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3178382 · Cited by 83 · see citing works
“Inclusive consensus processes for the standardized validation, benchmarking, and reporting of MML systems may play a vital role in these regards [254], [433], [434].”
BackgroundFarah, L., Murris, J., Borget, I., Guilloux, A., Martelli, N., & Katsahian, S. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.02.004 · Cited by 75 · see citing works
Rashidi, H. H., Pantanowitz, J., Hanna, M. G., Tafti, A. P., Sanghani, P., Buchinsky, A., Fennell, B. D., Deebajah, M., Wheeler, S., Pearce, T. M., et al. (2025) · Modern Pathology · doi:10.1016/j.modpat.2024.100688 · Cited by 72 · see citing works
Kainec, K. A., Caccavaro, J., Barnes, M., Hoff, C., Berlin, A., & Spencer, R. M. C. (2024) · Sensors · doi:10.3390/s24020635 · Cited by 66 · see citing works
Martínez-Millana, A., Saez-Saez, A., Tornero-Costa, R., Muscat, N. A., Traver, V., & Novillo-Ortiz, D. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104855 · Cited by 73 · see citing works
A critical moment in machine learning in medicine: on reproducible and interpretable learning
Ciobanu-Caraus, O., Aicher, A., Kernbach, J. M., Regli, L., Serra, C., & Staartjes, V. E. (2024) · Acta Neurochirurgica · doi:10.1007/s00701-024-05892-8 · Cited by 77 · see citing works
“MI-CLAIM (minimum information about clinical artificial intelligence modeling) [49] is a six-part-checklist to ensure transparency and inter-pretability of ML studies by establishing standard minimum requirements for study design, regarding the clinical setting, performance measures, population…”
Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer
Bitterman, D. S., Miller, T. A., Mak, R. H., & Savova, G. (2021) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2021.01.044 · Cited by 68 · see citing works
Shelmerdine, S. C., Arthurs, O. J., Denniston, A. K., & Sebire, N. J. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100385 · Cited by 67 · see citing works
Machine Learning in Laboratory Medicine: Recommendations of the IFCC Working Group
Master, S. R., Badrick, T. C., Bietenbeck, A., & Haymond, S. (2023) · Clinical Chemistry · doi:10.1093/clinchem/hvad055 · Cited by 64 · see citing works
AI Reporting Guidelines: How to Select the Best One for Your Research
Klontzas, M. E., Gatti, A. A., Tejani, A. S., & Kahn, C. E. (2023) · Radiology: Artificial Intelligence · doi:10.1148/ryai.230055 · Cited by 66 · see citing works
Singh, H., Mhasawade, V., & Chunara, R. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000023 · Cited by 67 · see citing works
“An example is the proposal for reporting subgroup-level performances in MI-CLAIM checklist [76].”
MethodReporting guidelines for artificial intelligence in healthcare research
Ibrahim, H. F., Liu, X., & Denniston, A. K. (2021) · Clinical and Experimental Ophthalmology · doi:10.1111/ceo.13943 · Cited by 63 · see citing works
“40 As with diagnostic accuracy studies, these guidelines may be supplemented by the valuable AI-specific guidance found in guidelines such as MI-CLAIM.”
MethodKlement, W., & Emam, K. E. (2023) · Journal of Medical Internet Research · doi:10.2196/48763 · Cited by 61 · see citing works
A scoping review on generative AI and large language models in mitigating medication related harm
Ong, J. C. L., Chen, M. H., Ng, N., Elangovan, K., Tan, N. Y. T., Jin, L., Xie, Q., Ting, D. S. W., Rodriguez-Monguio, R., Bates, D. W., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01565-7 · Cited by 64 · see citing works
Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs
Pyrros, A. T., Borstelmann, S. M., Mantravadi, R., Zaiman, Z., Thomas, K., Price, B., Greenstein, E., Siddiqui, N., Willis, M., Shulhan, I., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39631-x · Cited by 60 · see citing works
“The Checklist for Arti fi cial Intelligence in Medical Imaging was used for reporting in this study 47 .”
On the State of Social Media Data for Mental Health Research
Harrigian, K., Aguirre, C., & Dredze, M. H. (2021) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.18653/v1/2021.clpsych-1.2 · Cited by 63 · see citing works
“As researchers look to transition computational models into the clinical setting, it is imperative they have access to standardized benchmarks that inform interpretation of predictive results in a consistent manner (Norgeot et al., 2020).”
BackgroundIdeal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible
Loftus, T. J., Tighe, P. J., Ozrazgat‐Baslanti, T., Davis, J. P., Ruppert, M. M., Ren, Y., Shickel, B., Kamaleswaran, R., Hogan, W. R., Moorman, J. R., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000006 · Cited by 57 · see citing works
“We note, though, recent studies showing no great superiority of deep learning over regression in this field of classifying illness severity of individual patients using readily available clinical data [15,17].”
Niemiec, E. (2022) · Digital Health · doi:10.1177/20552076221089079 · Cited by 58 · see citing works
Stafford, I. S., Gosink, M. M., Mossotto, E., Ennis, S., & Hauben, M. (2022) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izac115 · Cited by 56 · see citing works
The potential role for artificial intelligence in fracture risk prediction
Hong, N., Whittier, D. E., Glüer, C.-C., & Leslie, W. D. (2024) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(24)00153-0 · Cited by 53 · see citing works
Farook, T. H., Jamayet, N. B., Abdullah, J. Y., & Alam, M. K. (2021) · Pain Research and Management · doi:10.1155/2021/6659133 · Cited by 56 · see citing works
“0e appropriateness of the machine learning model was evaluated using the Minimum Information about Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist [28].”
MethodGuidelines and standard frameworks for artificial intelligence in medicine: a systematic review
Shiferaw, K. B., Roloff, M., Balaur, I., Welter, D., Waltemath, D., & Zeleke, A. A. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae155 · Cited by 57 · see citing works
New horizons at the interface of artificial intelligence and translational cancer research
Yates, J., & Allen, E. M. V. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.03.018 · Cited by 55 · see citing works
The AIMe registry for artificial intelligence in biomedical research
Matschinske, J., Alcaraz, N., Benis, A., Golebiewski, M., Grimm, D. G., Heumos, L., Kacprowski, T., Lazareva, O., List, M., Louadi, Z., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01241-0 · Cited by 52 · see citing works
Farook, T. H., & Dudley, J. (2023) · Journal of Oral Rehabilitation · doi:10.1111/joor.13440 · Cited by 58 · see citing works
Artificial intelligence in breast ultrasonography
Kim, J., Kim, H. J., Kim, C., & Kim, W. H. (2020) · ULTRASONOGRAPHY · doi:10.14366/usg.20117 · Cited by 52 · see citing works
“Although most studies have used more than one image per patient for training and validation, specific details on the number of patients and images are needed based on the recently proposed Checklist for Artificial Intelligence in Medical Imaging [16].”
MethodLu, J., Callahan, A., Patel, B., Morse, K., Dash, D., Pfeffer, M. A., & Shah, N. H. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.27779 · Cited by 52 · see citing works
MAIC–10 brief quality checklist for publications using artificial intelligence and medical images
Cerdá-Alberich, L., Solana, J., Mallol, P., Ribas, G., García-Junco, M., Alberich‐Bayarri, Á., & Martí‐Bonmatí, L. (2023) · Insights into Imaging · doi:10.1186/s13244-022-01355-9 · Cited by 55 · see citing works
“Overall, 10 criteria were finally proposed after reviewing the selected guidelines [5, 7–11] and summarised into a concise MAIC-10 checklist (Table 1), which allows the straightforward calculation of a quality score.”
Transparency of medical artificial intelligence systems
Kim, C., Gadgil, S. U., & Lee, S. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00363-w · Cited by 58 · see citing works
“These include the MI-CLAIM Checklist 59 , the MINIMAR Reporting Standards 60 , the STANDING Together initiative 61 and the TRIPOD + AI statement 62 , paralleling initiatives in general AI domains 63–65 that suggest labels essential to understanding potential biases during model training.”
Afshar, M., Adelaine, S., Resnik, F., Mundt, M. P., Long, J., Leaf, M., Ampian, T., Wills, G. J., Schnapp, B., Chao, M., et al. (2023) · JMIR Medical Informatics · doi:10.2196/44977 · Cited by 44 · see citing works
“The Clinical AI and Predictive Analytics Committee follows the Minimum Information About Clinical AI Modeling checklist [40].”
Echle, A., Laleh, N. G., Schrammen, P. L., West, N. P., Trautwein, C., Brinker, T. J., Gruber, S. B., Bülow, R. D., Boor, P., Grabsch, H. I., et al. (2021) · ImmunoInformatics · doi:10.1016/j.immuno.2021.100008 · Cited by 50 · see citing works
Machine Learning in Clinical Trials: A Primer with Applications to Neurology
Miller, M. I., Shih, L. C., & Kolachalama, V. B. (2023) · Neurotherapeutics · doi:10.1007/s13311-023-01384-2 · Cited by 52 · see citing works
“The academic community has begun to develop reporting and protocol development guidelines for clinical trials involving AI [82, 179, 180], thus contributing to a culture of accountability surrounding medical ML among researchers.”
Background
- 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
- 632
- References
- 23
- Citations by year
- 2026: 112 · 2025: 106 · 2024: 126 · 2023: 105 · 2022: 93 · 2021: 60 · 2020: 9
- FWCI
- 16.40
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- FAIRsharing.org
- 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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-05.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-05.
Cited sources are checked by 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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