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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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]
Experimental — accuracy not yet measuredThe 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
“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
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., Vandenbroucke, J. P., & Initiative, F. T. S. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040296 · Cited by 23,020
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., Vandenbroucke, J. P., & Initiative, F. T. S. (2007) · Annals of Internal Medicine · doi:10.7326/0003-4819-147-8-200710160-00010 · Cited by 17,796
Moher, D., Hopewell, S., Schulz, K. F., Montori, V., Gotzsche, P. C., Devereaux, P. J., Elbourne, D., Egger, M., & Altman, D. G. (2010) · BMJ · doi:10.1136/bmj.c869 · Cited by 8,883
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137
Self-citationLipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648
Self-citationOpportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
Self-citationScalable and accurate deep learning with electronic health records.
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242
Self-citation
Research works printed but not linked: 3
- Subbaswamy, A. & Saria, S. Biostatistics 21, 345–352 (2020).
- Pan, J., McGuinness, K., Sayrol, E., O’Connor, N. & Giro-i-Nieto, X. arXiv https://ui.adsabs.harvard.edu/abs/2016arXiv160300845P (2016).
- Lundberg, S. & Lee, S.-I. arXiv https://ui.adsabs.harvard.edu/abs/2017arXiv170507874L (2017).
Cited by
Sorted by citations
Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-078378 · Cited by 4,718
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823
Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01772-9 · Cited by 1,039
“…AI-based diagnostic decision support systems 3 ; (2) an additional literature search about existing guidance for AI evaluation in clinical settings (search strategy available on the OSF 44 ); (3) literature recommended by Steering Group members 19,22,45–49 ; and (4) institutional documents 50–53 .”
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Varoquaux, G. P., & Cheplygina, V. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00592-y · Cited by 815
“Such initiatives should ideally be led by more established academics, and be welcoming of newcomers 104 .”
Challenges and advances in clinical applications of mesenchymal stromal cells
Zhou, T., Yuan, Z., Weng, J., Pei, D., Du, X., He, C., & Lai, P. (2021) · Journal of Hematology & Oncology · doi:10.1186/s13045-021-01037-x · Cited by 712
Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · BMJ · doi:10.1136/bmj-2022-070904 · Cited by 665
Self-citationThe TRIPOD-LLM reporting guideline for studies using large language models
Gallifant, J., Afshar, M., Ameen, S., Aphinyanaphongs, Y., Chen, S., Cacciamani, G., Demner-Fushman, D., Dligach, D., Daneshjou, R., Fernandes, C., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03425-5 · Cited by 615
“This is in addition to other guidelines that offer complementary guidance on AI development throughout the model life cycle 12–14 .”
de Hond, A. A. H., Leeuwenberg, A. M., Hooft, L., Kant, I. M. J., Nijman, S. W. J., van Os, H. J. A., Aardoom, J. J., Debray, T. P. A., Schuit, E., van Smeden, M., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-021-00549-7 · Cited by 499
“The literature stresses that detailed description of any preprocessing steps applied to the raw data should be provided, including software used to perform the processing steps 3,6,7,9,13 – 15, 22, 50, 61, 62, Data coding standards .”
BackgroundMethodInfluentialWu, E., Wu, K., Daneshjou, R., Ouyang, D., Ho, D. E., & Zou, J. (2021) · Nature Medicine · doi:10.1038/s41591-021-01312-x · Cited by 494
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Shmatko, A., Ghaffari Laleh, N., Gerstung, M., & Kather, J. N. (2022) · Nature Cancer · doi:10.1038/s43018-022-00436-4 · Cited by 446
“Rigorous standards should already be applied to preclinical and early clinical work; and indeed this is increasingly mandated by AI-specific reporting guidelines for scientific studies 147,148 .”
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Ma, J., Zhang, Y., Gu, S., Zhu, C. G., Ge, C., Zhang, Y., An, X., Wang, C., Wang, Q., Liu, X., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3100536 · Cited by 429
“reach high accuracy, but also can generalize well on data from different sources [8], [9].”
BackgroundTam, T. Y. C., Sivarajkumar, S., Kapoor, S., Stolyar, A. V., Polanska, K., McCarthy, K. R., Osterhoudt, H., Wu, X., Visweswaran, S., Fu, S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01258-7 · Cited by 424
Artificial intelligence for clinical oncology
Kann, B. H., Hosny, A., & Aerts, H. J. W. L. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.04.002 · Cited by 404
SHIFTing artificial intelligence to be responsible in healthcare: a systematic review
Siala, H., & Wang, Y. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114782 · Cited by 358
A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories
Placido, D., Yuan, B., Hjaltelin, J. X., Zheng, C., Haue, A. D., Chmura, P. J., Yuan, C., Kim, J., Umeton, R., Antell, G., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02332-5 · Cited by 349
Causal machine learning for predicting treatment outcomes
Feuerriegel, S., Frauen, D., Melnychuk, V., Schweisthal, J., Heß, K., Curth, A., Bauer, S., Kilbertus, N., Kohane, I. S., & van der Schaar, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-02902-1 · Cited by 353
“While there are checklists for traditional, predictive ML 96 and for generating real-world evidence 88,97 , future research is needed that adapts such checklists to account for the needs of causal ML in medicine.”
Self-citationCLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Liu, J., Zhang, Y., Chen, J.-N., Xiao, J., Lu, Y., Landman, B. A., Yuan, Y., Yuille, A., Tang, Y., & Zhou, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01934 · Cited by 381
“Furthermore, we validate several clinically important merits of the CLIP-Driven Universal Model, including compelling efficiency, generalizability, transfer-ability, and expansibility, through experimental results.”
MethodEthical and regulatory challenges of large language models in medicine
Ong, J. C. L., Chang, S. Y.-H., William, W., Butte, A. J., Shah, N. H., Chew, L. S. T., Liu, N., Doshi-Velez, F., Lu, W., Savulescu, J., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00061-x · Cited by 342
Machine Learning and the Future of Cardiovascular Care
Quer, G., Arnaout, R., Henne, M., & Arnaout, R. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.11.030 · Cited by 353
“In order to evaluate performance of any ML algorithm on a clinical task, full information on the data and methods used in training and testing must be reported (11).”
BackgroundMethodSelf-citationArnaout, R., Curran, L., Zhao, Y., Levine, J. C., Chinn, E., & Moon-Grady, A. J. (2020) · doi:10.1101/2020.06.22.20137786 · Cited by 315
“The methods below describe datasets, data processing, algorithms and performance measures 46 .”
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The need to separate the wheat from the chaff in medical informatics
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“…development transparency have been proposed that highlight the importance of cohort characteristics and reproducibility to target populations, 66,67 to the best of our knowledge, no consensus standards or guidelines specifically exist for the publication of skin cancer image datasets that have…”
BackgroundOgier du Terrail, J., Leopold, A., Joly, C., Béguier, C., Andreux, M., Maussion, C., Schmauch, B., Tramel, E. W., Bendjebbar, E., Zaslavskiy, M., et al. (2023) · Nature Medicine · doi:10.1038/s41591-022-02155-w · Cited by 217
“We follow the recent MI-Claim checklist 65 to improve the reporting of our ML methods.”
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Smets, J., Shevroja, E., Hügle, T., Leslie, W. D., & Hans, D. B. (2020) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.4292 · Cited by 207
“Suggested solutions include statements and checklists to improve and standardize the quality of reporting in ML studies.((22,116)) Peer-review journals have obligatory checklists to complete or follow before submission, such as STROBE for human observational studies,((117)) CONSORT for randomized controlled trials,((1…”
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Lisacek-Kiosoglous, A. B., Powling, A. S., Fontalis, A., Gabr, A., Mazomenos, E., & Haddad, F. S. (2023) · Bone & Joint Research · doi:10.1302/2046-3758.127.bjr-2023-0111.r1 · Cited by 184
Artificial intelligence-based clinical decision support in pediatrics
Ramgopal, S., Sanchez-Pinto, L. N., Horvat, C. M., Carroll, M. S., Luo, Y., & Florin, T. A. (2022) · Pediatric Research · doi:10.1038/s41390-022-02226-1 · Cited by 187
“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).”
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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
Where Medical Statistics Meets Artificial Intelligence
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“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.”
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APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support
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“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
“…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 149
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 137
“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., 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 122
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 137
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 113
“…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 Dental Research · doi:10.1177/00220345241235606 · Cited by 131
“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
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 133
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 118
“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
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 103
“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
“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 103
“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 116
“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 94
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 94
“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 134
“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 100
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 92
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
“…& 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 86
“…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 98
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 88
“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 85
“…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
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 92
“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 92
“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 83
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
Kainec, K. A., Caccavaro, J., Barnes, M., Hoff, C., Berlin, A., & Spencer, R. M. C. (2024) · Sensors · doi:10.3390/s24020635 · Cited by 66
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 75
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 82
“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
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 70
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
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
Singh, H., Mhasawade, V., & Chunara, R. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000023 · Cited by 67
“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 64
“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 65
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
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 63
“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 73
“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 59
“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 71
Stafford, I. S., Gosink, M. M., Mossotto, E., Ennis, S., & Hauben, M. (2022) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izac115 · Cited by 64
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
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
“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
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 58
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 54
Farook, T. H., & Dudley, J. (2023) · Journal of Oral Rehabilitation · doi:10.1111/joor.13440 · Cited by 58
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
“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 55
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 56
“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 60
“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
“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 52
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 58
“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
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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