There is no such thing as a validated prediction model
Open accessAbstract
BACKGROUND: Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external validation sufficient to claim that a prediction model works well in the intended clinical context? MAIN BODY: We argue to the contrary because (1) patient populations vary, (2) measurement procedures vary, and (3) populations and measurements change over time. Hence, we have to expect heterogeneity in model performance between locations and settings, and across time. It follows that prediction models are never truly validated. This does not imply that validation is not important. Rather, the current focus on developing new models should shift to a focus on more extensive, well-conducted, and well-reported validation studies of promising models. CONCLUSION: Principled validation strategies are needed to understand and quantify heterogeneity, monitor performance over time, and update prediction models when appropriate. Such strategies will help to ensure that prediction models stay up-to-date and safe to support clinical decision-making.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
There are widespread concerns that the performance of AI tools in early validation studies may not reflect their use in practice.
“There are widespread concerns that the performance of these tools in early validation studies may not reflect their use in practice.19,20”
From Uptake of Generative AI Integrated With Electronic Health Records in US Hospitals (2025) · cites this paper as [19]
Heterogeneity in model performance should be expected based on differences in populations and measurement procedures between locations, settings, or time periods.
“Second, heterogeneity in model performance should be expected based on differences in populations and measurement procedures between locations, settings, or time periods.”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [91]
Miscalibration is often not just a problem of the model but a sign that we need to improve our understanding of the various contexts in which the model is validated and used.
“Miscalibration is often not just a problem of the model but a sign that we need to improve our understanding of the various contexts in which the model is validated and used.”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [91]
One successful external validation does not establish transportability to many other settings, and requiring external validation might lead to selective reporting of validation data.
“One successful external validation, however, does not establish transportability to many other settings, while such a requirement might lead to the selective reporting of validation data.100”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [100]
Using data from 2001 to 2011, EuroSCORE was shown to consistently overestimate mortality and its calibration deteriorated over time.
“Using data from 2001 to 2011, EuroSCORE was shown to consistently overestimate mortality and its calibration deteriorated over time.100”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [100]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147 · see citing works
Internal validation of predictive models
Steyerberg, E. W., Harrell, F. E., Borsboom, G. J. J. M., Eijkemans, M. J. C., Vergouwe, Y., & Habbema, J. D. F. (2001) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(01)00341-9 · Cited by 2,746 · see citing works
Calibration: the Achilles heel of predictive analytics
On behalf of Topic Group ‘Evaluating diagnostic tests and prediction models’ of the STRATOS initiative, Van Calster, B., McLernon, D. J., van Smeden, M., Wynants, L., & Steyerberg, E. W. (2019) · BMC Medicine · doi:10.1186/s12916-019-1466-7 · Cited by 2,650 · see citing works
Anderson, D., Rodger, M., Ginsberg, J., Kearon, C., Gent, M., Turpie, A., Bormanis, J., Weitz, J., Chamberlain, M., Bowie, D., et al. (2000) · Thrombosis and Haemostasis · doi:10.1055/s-0037-1613830 · Cited by 1,651 · see citing works
Prognosis and prognostic research: validating a prognostic model
Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009) · BMJ · doi:10.1136/bmj.b605 · Cited by 1,436 · see citing works
Prediction models need appropriate internal, internal-external, and external validation
Steyerberg, E. W., & Harrell, F. E. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.04.005 · Cited by 1,304 · see citing works
Assessing the Generalizability of Prognostic Information
Justice, A. C., Covinsky, K. E., & Berlin, J. A. (1999) · Annals of Internal Medicine · doi:10.7326/0003-4819-130-6-199903160-00016 · Cited by 1,160 · see citing works
A calibration hierarchy for risk models was defined: from utopia to empirical data
Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina, M. J., & Steyerberg, E. W. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.12.005 · Cited by 910 · see citing works
Self-citationUnderstanding receiver operating characteristic (ROC) curves
Fan, J., Upadhye, S., & Worster, A. (2006) · Canadian Journal of Emergency Medicine · doi:10.1017/s1481803500013336 · Cited by 843 · see citing works
Collins, G. S., Ogundimu, E. O., & Altman, D. G. (2016) · Statistics in Medicine · doi:10.1002/sim.6787 · Cited by 665 · see citing works
Riley, R. D., Ensor, J., Snell, K. I. E., Debray, T. P. A., Altman, D. G., Moons, K. G. M., & Collins, G. S. (2016) · BMJ · doi:10.1136/bmj.i3140 · Cited by 585 · see citing works
Steyerberg, E. W., Wiegers, E., Sewalt, C. A., Büki, A., Citerio, G., Keyser, V. D., Ercole, A., Kunzmann, K., Lanyon, L., Lecky, F. E., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30232-7 · Cited by 499 · see citing works
The myth of generalisability in clinical research and machine learning in health care
Futoma, J., Simons, M., Panch, T., Doshi-Velez, F., & Celi, L. A. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30186-2 · Cited by 467 · see citing works
Minimum sample size for external validation of a clinical prediction model with a binary outcome
Riley, R. D., Debray, T. P. A., Collins, G. S., Archer, L., Ensor, J., van Smeden, M., & Snell, K. I. E. (2021) · Statistics in Medicine · doi:10.1002/sim.9025 · Cited by 400 · see citing works
Agniel, D., Kohane, I. S., & Weber, G. M. (2018) · BMJ · doi:10.1136/bmj.k1479 · Cited by 391 · see citing works
Calibration of Risk Prediction Models: Impact on Decision-Analytic Performance
Van Calster, B., & Vickers, A. J. (2015) · Medical Decision Making · doi:10.1177/0272989x14547233 · Cited by 371 · see citing works
Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2014.11.010 · Cited by 333 · see citing works
Calibration drift in regression and machine learning models for acute kidney injury
Davis, S. E., Lasko, T. A., Chen, G., Siew, E. D., & Matheny, M. E. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx030 · Cited by 312 · see citing works
Deep learning predicts hip fracture using confounding patient and healthcare variables
Badgeley, M. A., Zech, J. R., Oakden-Rayner, L., Glicksberg, B. S., Liu, M., Gale, W., McConnell, M. V., Percha, B., Snyder, T. M., & Dudley, J. T. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0105-1 · Cited by 290 · see citing works
A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes
Debray, T. P., Damen, J. A., Riley, R. D., Snell, K., Reitsma, J. B., Hooft, L., Collins, G. S., & Moons, K. G. (2019) · Statistical Methods in Medical Research · doi:10.1177/0962280218785504 · Cited by 278 · see citing works
Binuya, M. A. E., Engelhardt, E. G., Schats, W., Schmidt, M. K., & Steyerberg, E. W. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01801-8 · Cited by 246 · see citing works
Predictive analytics in health care: how can we know it works?
Van Calster, B., Wynants, L., Timmerman, D., Steyerberg, E. W., & Collins, G. S. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz130 · Cited by 223 · see citing works
Debray, T. P. A., Moons, K. G. M., Ahmed, I., Koffijberg, H., & Riley, R. D. (2013) · Statistics in Medicine · doi:10.1002/sim.5732 · Cited by 215 · see citing works
Minimum sample size for external validation of a clinical prediction model with a continuous outcome
Archer, L., Snell, K. I. E., Ensor, J., Hudda, M. T., Collins, G. S., & Riley, R. D. (2021) · Statistics in Medicine · doi:10.1002/sim.8766 · Cited by 198 · see citing works
Systematic review of prediction models for delirium in the older adult inpatient
Lindroth, H. L., Bratzke, L. C., Purvis, S., Brown, R., Coburn, M., Mrkobrada, M., Chan, M. T. V., Davis, D. H. J., Pandharipande, P. P., Carlsson, C. M., et al. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-019223 · Cited by 197 · see citing works
Detection of calibration drift in clinical prediction models to inform model updating
Davis, S. E., Greevy, R. A., Lasko, T. A., Walsh, C. G., & Matheny, M. E. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103611 · Cited by 160 · see citing works
Hickey, G. L., Grant, S. W., Murphy, G. J., Bhabra, M., Pagano, D., McAllister, K., Buchan, I., & Bridgewater, B. (2013) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezs584 · Cited by 154 · see citing works
Informative missingness in electronic health record systems: the curse of knowing
Groenwold, R. (2020) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-00077-0 · Cited by 139 · see citing works
Fesmire, F. M., Brown, M. D., Espinosa, J. A., Shih, R. D., Silvers, S. M., Wolf, S. J., & Decker, W. W. (2011) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2011.01.020 · Cited by 136 · see citing works
Jenkins, D. A., Martin, G. P., Sperrin, M., Riley, R. D., Debray, T. P. A., Collins, G. S., & Peek, N. (2021) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-00090-3 · Cited by 131 · see citing works
CALSTER, B. V., Valentin, L., Froyman, W., Landolfo, C., Ceusters, J., Testa, A. C., Wynants, L., Sladkevicius, P., Holsbeke, C. V., Domali, E., et al. (2020) · BMJ · doi:10.1136/bmj.m2614 · Cited by 128 · see citing works
Min, J. H., Lee, M. W., Park, H. S., Lee, D. H., Park, H. J., Lim, S., Choi, S., Lee, J., Lee, J. E., Ha, S. Y., et al. (2020) · Radiology · doi:10.1148/radiol.2020201940 · Cited by 123 · see citing works
Cook, G., Royle, K., Pawlyn, C., Hockaday, A., Shah, V., Kaiser, M., Brown, S., Gregory, W. M., Child, J. A., Davies, F. E., et al. (2019) · The Lancet Haematology · doi:10.1016/s2352-3026(18)30220-5 · Cited by 113 · see citing works
Luijken, K., Groenwold, R., van Calster, B., Steyerberg, E., & van Smeden, M. (2019) · Statistics in Medicine · doi:10.1002/sim.8183 · Cited by 103 · see citing works
Austin, P. C., Klaveren, D. V., Vergouwe, Y., Nieboer, D., Lee, D. S., & Steyerberg, E. W. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.05.007 · Cited by 98 · see citing works
Debray, T. P. A., Collins, P. G. S., Riley, R. D., Snell, K. I. E., CALSTER, B. V., Reitsma, J. B., & Moons, K. G. M. (2023) · BMJ · doi:10.1136/bmj-2022-071018 · Cited by 97 · see citing works
Prognostic models will be victims of their own success, unless…
Lenert, M. C., Matheny, M. E., & Walsh, C. G. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz145 · Cited by 90 · see citing works
Takada, T., Nijman, S. W. J., Denaxas, S., Snell, K. I. E., Uijl, A., Nguyen, T., Asselbergs, F. W., & Debray, T. P. A. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.03.025 · Cited by 89 · see citing works
Nestor, B. A., McDermott, M. B. A., Boag, W., Berner, G., Naumann, T., Hughes, M. C., Goldenberg, A., & Ghassemi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.00690 · Cited by 78 · see citing works
Steyerberg, E. W., Nieboer, D., Debray, T. P. A., & van Houwelingen, H. C. (2019) · Statistics in Medicine · doi:10.1002/sim.8296 · Cited by 73 · see citing works
de Jong, V. M. T., Rousset, R. Z., Antonio‐Villa, N. E., Buenen, A. G., CALSTER, B. V., Bello‐Chavolla, O. Y., Brunskill, N. J., Ćurčin, V., Damen, J. A., Fermín‐Martínez, C. A., et al. (2022) · BMJ · doi:10.1136/bmj-2021-069881 · Cited by 64 · see citing works
Debray, T. P. A., Collins, P. G. S., Riley, R. D., Snell, K. I. E., CALSTER, B. V., Reitsma, J. B., & Moons, K. G. M. (2023) · BMJ · doi:10.1136/bmj-2022-071058 · Cited by 62 · see citing works
Gulati, G., Upshaw, J., Wessler, B. S., Brazil, R. J., Nelson, J., Klaveren, D. V., Lundquist, C. M., Park, J. G., McGinnes, H., Steyerberg, E. W., et al. (2022) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.121.008487 · Cited by 60 · see citing works
Stiell, I. G., Clement, C. M., O'Connor, A. M. C., Davies, B., Leclair, C., Sheehan, P. J., Clavet, T., Beland, C., MacKenzie, T., & Wells, G. A. (2010) · Canadian Medical Association Journal · doi:10.1503/cmaj.091430 · Cited by 53 · see citing works
Luijken, K., Wynants, L., van Smeden, M., CALSTER, B. V., Steyerberg, E. W., Groenwold, R. H. H., Timmerman, D., Bourne, T., & Ukaegbu, C. I. (2019) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.11.001 · Cited by 53 · see citing works
Austin, P. C., Klaveren, D. V., Vergouwe, Y., Nieboer, D., Lee, D. S., & Steyerberg, E. W. (2017) · Diagnostic and Prognostic Research · doi:10.1186/s41512-017-0012-3 · Cited by 48 · see citing works
Clinical experience and pre-test probability scores in the diagnosis of pulmonary embolism
Iles, S. (2003) · QJM · doi:10.1093/qjmed/hcg027 · Cited by 46 · see citing works
The heterogeneity of concentrated prescribing behavior: Theory and evidence from antipsychotics
Berndt, E. R., Gibbons, R. S., Kolotilin, A., & Taub, A. L. (2014) · Journal of Health Economics · doi:10.1016/j.jhealeco.2014.11.003 · Cited by 45 · see citing works
Wynants, L., Kent, D. M., Timmerman, D., Lundquist, C. M., & CALSTER, B. V. (2019) · Diagnostic and Prognostic Research · doi:10.1186/s41512-019-0046-9 · Cited by 45 · see citing works
Wynants, L., Vergouwe, Y., Huffel, S. V., Timmerman, D., & CALSTER, B. V. (2016) · Statistical Methods in Medical Research · doi:10.1177/0962280216668555 · Cited by 41 · see citing works
Kim, D., Chung, J. W., Choi, J., Succi, M. D., Conklin, J., Longo, M. G. F., Ackman, J. B., Little, B. P., Petranović, M., Kalra, M. K., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29437-8 · Cited by 40 · see citing works
Measurement error in biomarkers: sources, assessment, and impact on studies.
White, E. (2011) · PubMed · Cited by 36 · see citing works
Verification of the harmonization of human epididymis protein 4 assays
Ferraro, S., Borille, S., Carnevale, A., Frusciante, E., Bassani, N., & Panteghini, M. (2016) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2015-1142 · Cited by 28 · see citing works
Screening for data clustering in multicenter studies: the residual intraclass correlation
Wynants, L., Timmerman, D., Bourne, T., Huffel, S. V., & CALSTER, B. V. (2013) · BMC Medical Research Methodology · doi:10.1186/1471-2288-13-128 · Cited by 27 · see citing works
Is your clinical prediction model past its sell by date?
Reynard, C., Jenkins, D. A., Martin, G. P., Kontopantelis, E., & Body, R. M. (2022) · Emergency Medicine Journal · doi:10.1136/emermed-2021-212224 · Cited by 7 · see citing works
Flagged first, then by citations
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 · see citing works
“76 To avoid ambiguity and harmonise terminology, we refer to validation as evaluation 74 in this article (box 1).”
MethodEvaluation of clinical prediction models (part 1): from development to external validation
Collins, G. S., Dhiman, P., Ma, J., Schlussel, M. M., Archer, L., Van Calster, B., Harrell, F. E., Martin, G. P., Moons, K. G. M., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-074819 · Cited by 798 · see citing works
Developing clinical prediction models: a step-by-step guide
Efthimiou, O., Seo, M., Chalkou, K., Debray, T., Egger, M., & Salanti, G. (2024) · BMJ · doi:10.1136/bmj-2023-078276 · Cited by 544 · see citing works
Evaluation of clinical prediction models (part 2): how to undertake an external validation study
Riley, R., Archer, L., Snell, K., Ensor, J., Dhiman, P., Martin, G. P., Bonnett, L., & Collins, G. S. (2024) · British medical journal · doi:10.1136/bmj-2023-074820 · Cited by 515 · see citing works
(2024) · BMJ · doi:10.1136/bmj.q902 · Cited by 416 · see citing works
“76 To avoid ambiguity and harmonise terminology, we refer to validation as evaluation 74 in this article (box 1).”
External validation of AI models in health should be replaced with recurring local validation
Youssef, A., Pencina, M., Thakur, A., Zhu, T., Clifton, D., & Shah, N. H. (2023) · Nature Medicine · doi:10.1038/s41591-023-02540-z · Cited by 170 · 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
Evidence of questionable research practices in clinical prediction models
White, N. M., Parsons, R., Collins, P. G. S., & Barnett, A. G. (2023) · BMC Medicine · doi:10.1186/s12916-023-03048-6 · Cited by 149 · see citing works
Generalizability assessment of AI models across hospitals in a low-middle and high income country
Yang, J., Dung, N. T., Thạch, P. N., Phong, N. T., Phu, V. D., Dong, P. K., Yen, L. M., Thy, D. B. X., Soltan, A. A. S., Thwaites, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52618-6 · Cited by 110 · see citing works
Nong, P., Adler-Milstein, J., Apathy, N. C., Holmgren, A. J., & Everson, J. (2025) · Health Affairs · doi:10.1377/hlthaff.2024.00842 · Cited by 104 · see citing works
Deisenhofer, A., Barkham, M., Beierl, E. T., Schwartz, B., Doorn, K. A., Beevers, C. G., Berwian, I. M., Blackwell, S. E., Bockting, C., Brakemeier, E., et al. (2023) · Behaviour Research and Therapy · doi:10.1016/j.brat.2023.104443 · Cited by 115 · see citing works
Van Calster, B., Collins, G. S., Vickers, A. J., Wynants, L., Kerr, K. F., Barreñada, L., Varoquaux, G., Singh, K., Moons, K. G., Hernandez-Boussard, T., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100916 · Cited by 112 · see citing works
Perspectives on validation of clinical predictive algorithms
de Hond, A., Shah, V. B., Kant, I. M. J., CALSTER, B. V., Steyerberg, E. W., & Hernandez‐Boussard, T. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00832-9 · Cited by 95 · see citing works
Five critical quality criteria for artificial intelligence-based prediction models
van Royen, F. S.-A., Asselbergs, F. W., Alfonso, F., Vardas, P. E., & van Smeden, M. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad727 · Cited by 77 · see citing works
Liu, K., Li, L., Ma, Y., Jiang, J., Liu, Z., Ye, Z., Liu, S., Pu, C., Chen, C., & Wan, Y. (2023) · JMIR Medical Informatics · doi:10.2196/47833 · Cited by 68 · see citing works
External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis
Rockenschaub, P., Akay, E. M., Carlisle, B. G., Hilbert, A., Wendland, J., Meyer-Eschenbach, F., Näher, A.-F., Frey, D., & Madai, V. I. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02830-7 · Cited by 58 · see citing works
Don't be misled: 3 misconceptions about external validation of clinical prediction models
la Roi-Teeuw, H. M., van Royen, F. S.-A., de Hond, A., Zahra, A., de Vries, S., Bartels, R., Carriero, A., van Doorn, S., Dunias, Z. S., Kant, I. M. J., et al. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111387 · Cited by 57 · see citing works
Egemen, D., Perkins, R. B., Cheung, L. C., Befano, B., Rodríguez, A. C., Desai, K. T., Lemay, A., Ahmed, S. R., Antani, S., Jerónimo, J., et al. (2023) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djad202 · Cited by 55 · see citing works
Ling, X. C., Chen, H. S.-L., Yeh, P., Cheng, Y.-C., Huang, C.-Y., Shen, S., & Lee, Y. (2025) · Biomedicines · doi:10.3390/biomedicines13020420 · Cited by 59 · see citing works
Sustainable deployment of clinical prediction tools—a 360° approach to model maintenance
Davis, S. E., Embí, P. J., & Matheny, M. E. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae036 · Cited by 46 · see citing works
Externally validated prediction models for pre‐eclampsia: systematic review and meta‐analysis
Tiruneh, S. A., Vu, T. T. T., Moran, L., Callander, E. J., Allotey, J., Thangaratinam, S., Rolnik, D. L., Teede, H. J., Wang, R., & Enticott, J. (2023) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.27490 · Cited by 47 · see citing works
Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings
Vaid, A., Sawant, A. S., Suárez‐Fariñas, M., Lee, J., Kaul, S. K., Kovatch, P., Freeman, R., Jiang, J. J., Jayaraman, P., Fayad, Z. A., et al. (2023) · Annals of Internal Medicine · doi:10.7326/m23-0949 · Cited by 46 · see citing works
Barreñada, L., Ledger, A., Dhiman, P., Collins, G., Wynants, L., Verbakel, J. Y., Timmerman, D., Valentin, L., & Van Calster, B. (2024) · BMJ Medicine · doi:10.1136/bmjmed-2023-000817 · Cited by 42 · see citing works
AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges
Okada, Y., Mertens, M., Liu, N., Lam, S. S. W., & Ong, M. E. H. (2023) · Resuscitation Plus · doi:10.1016/j.resplu.2023.100435 · Cited by 41 · see citing works
Evaluating transparency in AI/ML model characteristics for FDA-reviewed medical devices
Mehta, V., Komanduri, A., Bhadouriya, R. S., Mehta, V., Johnson, M. D., Shrestha, P., Nikolov, M. C., Jain, B., Shah, N. H., & Schulman, K. A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02052-9 · Cited by 45 · see citing works
Why do probabilistic clinical models fail to transport between sites
Lasko, T. A., Strobl, E. V., & Stead, W. W. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01037-4 · Cited by 34 · see citing works
Uptake of Generative AI Integrated With Electronic Health Records in US Hospitals
Everson, J., Nong, P., & Richwine, C. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.49463 · Cited by 33 · see citing works
Prognosticating the outcome of intensive care in older patients—a narrative review
Beil, M., Moreno, R. P., Fronczek, J., Kogan, Y., Moreno, R. P., Flaatten, H. K., Guidet, B., de Lange, D. W., Leaver, S. K., Nachshon, A., et al. (2024) · Annals of Intensive Care · doi:10.1186/s13613-024-01330-1 · Cited by 29 · see citing works
Deep learning for electrocardiogram interpretation: Bench to bedside
Schots, B. B. S., Pizarro, C. S., Arends, B. K. O., Oerlemans, M. I. F. J., Ahmetagić, D., van der Harst, P., & van Es, R. (2025) · European Journal of Clinical Investigation · doi:10.1111/eci.70002 · Cited by 28 · see citing works
Clinical data mining: challenges, opportunities, and recommendations for translational applications
Qiao, H., Chen, Y., Qian, C., & Guo, Y. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05005-0 · Cited by 27 · see citing works
Jain, S. S., Goto, S., Hall, J. L., Khan, S. S., MacRae, C. A., Ofori, C., Pegus, C., Pencina, M., Peterson, E. D., Schwamm, L. H., et al. (2025) · Circulation · doi:10.1161/cir.0000000000001400 · Cited by 23 · see citing works
Ratna, M. B., Bhattacharya, S., & McLernon, D. J. (2023) · Human Reproduction · doi:10.1093/humrep/dead165 · Cited by 23 · see citing works
Wang, Z., Wang, W., Sun, C., Li, J., Xie, S., Xu, J., Zou, K., Jin, Y., Yan, S., Liao, X., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01587-1 · Cited by 25 · see citing works
Luu, H. S. (2024) · Annals of Laboratory Medicine · doi:10.3343/alm.2024.0323 · Cited by 24 · see citing works
Prediction of Morbidity and Mortality After Esophagectomy: A Systematic Review
Amerongen, M. P. V. N., de Grooth, H., Veerman, G., Ziesemer, K. A., Henegouwen, M. I. V. B., & Tuinman, P. R. (2024) · Annals of Surgical Oncology · doi:10.1245/s10434-024-14997-4 · Cited by 22 · see citing works
Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction Model
Wong, A., Currey, D., Schwinne, M., Park-Egan, B., Meyer, S., Gutting, A., Cao, J., Khan, S., Dantes, R., Pan, T., et al. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.0181 · Cited by 21 · see citing works
Adeoye, J., & Su, Y. (2023) · International Journal of Surgery · doi:10.1097/js9.0000000000000979 · Cited by 21 · see citing works
Wang, X., Zhou, S., Ye, N., Li, Y., Zhou, P., Gao, C., & Hu, H. Z. (2024) · BMC Geriatrics · doi:10.1186/s12877-024-05044-8 · Cited by 22 · see citing works
Updating methods for artificial intelligence–based clinical prediction models: a scoping review
Meijerink, L., Dunias, Z. S., Leeuwenberg, A. M., de Hond, A., Jenkins, D. A., Martin, G. P., Sperrin, M., Peek, N. B., Spijker, R., Hooft, L., et al. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111636 · Cited by 19 · see citing works
Cabanillas Silva, P., Sun, H., Rezk, M., Roccaro-Waldmeyer, D. M., Fliegenschmidt, J., Hulde, N., von Dossow, V., Meesseman, L., Depraetere, K., Stieg, J., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/51409 · Cited by 20 · see citing works
Madathil, S. A., Dhouib, M., Lelong, Q., Bourassine, A., & Monsonégo, J. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109710 · Cited by 20 · see citing works
González, 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 21 · see citing works
Instability of the AUROC of clinical prediction models
van Leeuwen, F. D., Steyerberg, E. W., van Klaveren, D., Wessler, B., Kent, D. M., & van Zwet, E. W. (2025) · Statistics in Medicine · doi:10.1002/sim.70011 · Cited by 19 · see citing works
The Role of Artificial Intelligence in Chronic Liver Diseases and Liver Transplantation
Spann, A., Strauss, A. T., Davis, S. E., & Bhat, M. (2025) · Gastroenterology · doi:10.1053/j.gastro.2025.05.012 · Cited by 21 · see citing works
Detecting changes in the performance of a clinical machine learning tool over time
Schinkel, M., Boerman, A. W., Paranjape, K., Wiersinga, W. J., & Nanayakkara, P. W. B. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104823 · Cited by 19 · see citing works
Gonzalez, R., Nejat, P., Saha, A., Campbell, C. J. V., Norgan, A. P., & Lokker, C. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100348 · Cited by 18 · see citing works
Bryant, A. K., Zamora‐Resendiz, R., Dai, X., Morrow, D., Lin, Y., Jungles, K. M., Rae, J. M., Tate, A., Pearson, A. N., Jiang, R., et al. (2024) · Cancer Medicine · doi:10.1002/cam4.7253 · Cited by 18 · see citing works
Evaluating dental AI research papers: Key considerations for editors and reviewers
Uribe, S. E., Hamdan, M. H., Valente, N. A., Yamaguchi, S., Umer, F., Tichý, A., Pauwels, R., & Schwendicke, F. (2025) · Journal of Dentistry · doi:10.1016/j.jdent.2025.105867 · Cited by 16 · see citing works
In humble defense of unexplainable black box prediction models in healthcare
van Royen, F. S.-A., Weerts, H., de Hond, A., Geersing, G., Rutten, F. H., Moons, K. G. M., & van Smeden, M. (2025) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2025.112013 · Cited by 18 · see citing works
Suh, Y. J., Han, K., Kwon, Y., Kim, H., Lee, S., Hwang, S. H., Kim, M. H., Shin, H. J., Lee, C. Y., & Shim, H. S. (2024) · Yonsei Medical Journal · doi:10.3349/ymj.2023.0368 · Cited by 15 · see citing works
Alfaraj, S., Kist, J. M., Groenwold, R. H. H., Spruit, M. R., Mook-Kanamori, D. O., & Vos, R. C. (2024) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwae354 · Cited by 15 · see citing works
The validation of prediction models deserves more recognition
Mansmann, U., & Ön, B. I. (2025) · BMC Medicine · doi:10.1186/s12916-025-03994-3 · Cited by 16 · see citing works
Kyriacou, C., Ledger, A., Bobdiwala, S., Ayim, F., Kirk, E., Abughazza, O., Guha, S., Vathanan, V., Gould, D., Timmerman, D., et al. (2023) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.27515 · Cited by 15 · see citing works
Neal, S. R., Sturrock, S., Musorowegomo, D., Gannon, H., Zaman, M., Cortina‐Borja, M., Doaré, K. L., Heys, M., Chimhini, G., & Fitzgerald, F. (2025) · BMJ Global Health · doi:10.1136/bmjgh-2024-017582 · Cited by 14 · see citing works
Oakley, W., Tandle, S., Perkins, Z., & Marsden, M. E. R. (2024) · The Journal of Trauma: Injury, Infection, and Critical Care · doi:10.1097/ta.0000000000004385 · Cited by 14 · see citing works
van der Meijden, S. L., van Boekel, A. M., Schinkelshoek, L., van Goor, H., Steyerberg, E. W., Nelissen, R. G. H. H., Mesotten, D., Geerts, B. F., de Boer, M. G. J., Arbous, M. S., et al. (2024) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2024.101163 · Cited by 13 · see citing works
Gill, S. S., Ponniah, H. S., Giersztein, S., Anantharaj, R. M., Namireddy, S. R., Killilea, J., Ramsay, D. S. C., Salih, A. M., Thavarajasingam, A., Scurtu, D., et al. (2025) · Brain and Spine · doi:10.1016/j.bas.2025.104208 · Cited by 15 · see citing works
Landolfo, C., Ceusters, J., Valentin, L., Froyman, W., Van Gorp, T., Heremans, R., Baert, T., Wouters, R., Vankerckhoven, A., Van Rompuy, A.-S., et al. (2024) · British Journal of Cancer · doi:10.1038/s41416-024-02578-x · Cited by 14 · see citing works
External validation of 12 existing survival prediction models for patients with spinal metastases
Bindels, B. J. J., Kuijten, R. H., Groot, O. Q., Huele, E. H., Gal, R., de Groot, M., van der Velden, J. M., Delawi, D., Schwab, J. H., Verkooijen, H. M., et al. (2025) · The Spine Journal · doi:10.1016/j.spinee.2025.01.014 · Cited by 12 · see citing works
Good practices and common pitfalls of machine learning in nutrition research
Kirk, D. (2024) · Proceedings of The Nutrition Society · doi:10.1017/s0029665124007638 · Cited by 11 · see citing works
Negoi, I. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i18.106670 · Cited by 11 · see citing works
Contesting the conventional wisdom of periodontal risk assessment
Raittio, E. J., López, R., & Bælum, V. (2024) · Community Dentistry And Oral Epidemiology · doi:10.1111/cdoe.12942 · Cited by 11 · see citing works
de Winkel, J., Maas, C. C. H. M., Roozenbeek, B., Klaveren, D. V., & Lingsma, H. F. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02280-9 · Cited by 10 · see citing works
Diagnostic framework to validate clinical machine learning models locally on temporally stamped data
Schuessler, M., Fleming, S. L., Meyer, S., Seto, T., & Hernandez-Boussard, T. (2025) · Communications Medicine · doi:10.1038/s43856-025-00965-w · Cited by 8 · see citing works
CALSTER, B. V., van Smeden, M., van Amsterdam, W. A. C., Coemans, M., Wynants, L., & Steyerberg, E. W. (2025) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042324-123749 · Cited by 12 · see citing works
Kim, R. Y. (2024) · Cancer Biomarkers · doi:10.3233/cbm-230360 · Cited by 9 · see citing works
Shetty, M. S., Peña‐Corona, S. I., Borbolla‐Jiménez, F. V., Kaverikana, R., Shetty, S., Shet, V. B., Almarhoon, Z. M., Călina, D. C., Leyva‐Gómez, G., & Sharifi‐Rad, J. (2024) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-024-03095-7 · Cited by 9 · see citing works
Hong, M., Kang, R.-R., Yang, J. H., Rhee, S. J., Lee, H., Kim, Y.-G., Lee, K. Y., Kim, H., Lee, Y. S., Youn, T., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/65994 · Cited by 9 · see citing works
Sajanti, A., Hellström, S., Bennett, C., Srinath, A., Jhaveri, A., Cao, Y. J., Takala, R. S. K., Frantzén, J., Koskimäki, F., Falter, J., et al. (2024) · Inflammation · doi:10.1007/s10753-024-02185-1 · Cited by 9 · see citing works
The urgency of centering safety-net organizations in AI governance
Nong, P., Maurer, E. W., & Dwivedi, R. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01479-4 · Cited by 9 · see citing works
Balcarcel, D. R., Mehta, S. D., Dixon, C. G., Woods-Hill, C. Z., Goligher, E. C., van Amsterdam, W. A. C., & Yehya, N. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100880 · Cited by 9 · see citing works
Glans, M., Kempen, T. G. H., Jakobsson, U., Ekstam, A. K., Bondesson, Å., & Midlöv, P. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-070559 · Cited by 9 · see citing works
Ethical Pitfalls in AI‐Based Predictive Models in Surgery
Hmido, S. B., Rahim, H. A., Keller, B. P., Daams, F. D., Schakel, M., Goslings, J. C., van Dijkum, E. J. M. N., Rainey, S., Geert Kazemier, M., Bak, M., et al. (2025) · World Journal of Surgery · doi:10.1002/wjs.70080 · Cited by 8 · see citing works
van der Staaij, H., Prosepe, I., Caram‐Deelder, C., Keogh, R. H., Deschmann, E., Dame, C., Onland, W., Prins, S. A., Cassel, F., d’Haens, E. J., et al. (2025) · JAMA · doi:10.1001/jama.2025.14194 · Cited by 8 · see citing works
Park, S. H., & Hwang, E. J. (2024) · Korean Journal of Radiology · doi:10.3348/kjr.2024.0144 · Cited by 8 · see citing works
Nejad, M. C., Matthiesen, R. V., Dukovska‐Popovska, I., Jakobsen, T. L., & Johansen, J. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105631 · Cited by 7 · see citing works
Predicting periprosthetic joint infection: external validation of preoperative prediction models
Yoon, S. I., Jutte, P. C., Soriano, À., de Sousa, R. J. G., Zijlstra, W. P., & Wouthuyzen‐Bakker, M. (2024) · Journal of Bone and Joint Infection · doi:10.5194/jbji-9-231-2024 · Cited by 7 · see citing works
Yao, B., Yu, X., Zhang, Y., Chen, J., Zhu, X., Zhang, C., & Jijun, T. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1719631 · Cited by 10 · see citing works
Wernly, B., Guidet, B., & Beil, M. (2024) · Intensive Care Medicine · doi:10.1007/s00134-024-07738-4 · Cited by 7 · see citing works
Ajuwon, B. I., Richardson, A., Roper, K., & Lidbury, B. A. (2023) · Viruses · doi:10.3390/v15081735 · Cited by 6 · see citing works
Tack, B., Vita, D., Mbuyamba, J., Ntangu, E., Vuvu, H., Kahindo, I., Ngina, J., Luyindula, A., Nama, N., Mputu, T., et al. (2025) · BMC Infectious Diseases · doi:10.1186/s12879-024-10319-x · Cited by 7 · see citing works
Tiong, K. Y., Ma, Z., & Palmqvist, C.-W. (2025) · European Transport Research Review · doi:10.1186/s12544-024-00704-7 · Cited by 6 · see citing works
Multi‐Centre External Validation of a Nomogram for 10‐Year Periodontal Tooth Loss Prediction
Serroni, M., Ravidà, A., Santamaria, P., Dias, D. R., Cheng, G., Shaikh, S., Natanzi, N. D., Saleh, M. H. A., Nibali, L., & Troiano, G. (2025) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.14143 · Cited by 6 · see citing works
Nwokedi, V. U., Odedele, M. K., Yusuff, T. A., Anderson, I. A., Akanbi, O. O., Agu, C. P., & Omoike, A. O. (2025) · Epidemiology and Health Data Insights · doi:10.63946/ehdi/17369 · Cited by 6 · see citing works
Using routinely collected data for research purposes: challenges and mitigation strategies
Hoffmann, S., Morris, T. P., Herrmann, M., Heinze, G., Wynants, L., CALSTER, B. V., Bischl, B., Schmid, M., Shaw, P. A., Mathes, T., et al. (2026) · BMJ · doi:10.1136/bmj-2025-087812 · Cited by 7 · see citing works
Sherminie, L. P. G., Jayatilake, M. L., Hewavithana, B., Weerakoon, B. S., & Vijithananda, S. M. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1139902 · Cited by 6 · see citing works
Grotenhuis, Z., Mosteiro, P. J., & Leeuwenberg, A. M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108014 · Cited by 7 · see citing works
de Winkel, J., Roozenbeek, B., Dijkland, S. A., Dammers, R., van Doormaal, P., van der Jagt, M., Klaveren, D. V., Dippel, D. W. J., & Lingsma, H. F. (2024) · BMC Neurology · doi:10.1186/s12883-024-03546-x · Cited by 6 · see citing works
Tangel, V. E., Hoeks, S. E., Stolker, R. J., Brown, S. E. S., Pryor, K. O., de Graaff, J. C., Pace, N. L., Domino, K. B., Muehlschlegel, J. D., Kheterpal, S., et al. (2024) · British Journal of Anaesthesia · doi:10.1016/j.bja.2024.09.003 · Cited by 6 · see citing works
Shamsutdinova, D. D., Stamate, D., & Stahl, D. R. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105700 · Cited by 6 · see citing works
Hillier, B., Scandrett, K., Coombe, A., Hernandez‐Boussard, T., Steyerberg, E. W., Takwoingi, Y., Veličković, V. M., & Dinnes, J. (2025) · Diagnostic and Prognostic Research · doi:10.1186/s41512-024-00182-4 · Cited by 8 · see citing works
Dynamic Updating of Psychosis Prediction Models in Individuals at Ultra-High Risk of Psychosis
Hartmann, S., Dwyer, D., Scott, I., Wannan, C., Nguyen, J., Lin, A. P., Middeldorp, C. M., Wood, S. J., Yung, A. R., McGorry, P. D., et al. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.03.006 · Cited by 6 · see citing works
Obayes, M. Q., Nassrullah, A. H. A., Abas, A., Hameed, M., Alomairi, A. O. W., Nayyef, H. A., & Ahmed, S. R. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11292358 · Cited by 5 · see citing works
White, N. M., Parsons, R., Borg, D. N., Collins, P. G. S., & Barnett, A. G. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111433 · Cited by 5 · see citing works
Tian, C. H., Liu, L., Huang, Y. F., Yang, H. J., Lai, Y. Y., Li, C., Gan, D., & Yang, J. (2025) · Human Reproduction · doi:10.1093/humrep/deaf013 · Cited by 5 · see citing works
Drebin, H. M., Kurtansky, N. R., Hosein, S., Nadelmann, E. R., Moy, A. P., Ariyan, C. E., Bello, D. M., Brady, M. S., Coit, D. G., Marchetti, M. A., et al. (2024) · Annals of Surgical Oncology · doi:10.1245/s10434-024-16698-4 · Cited by 5 · see citing works
Gao, S., Albu, E., Putter, H., Stijnen, P., Rademakers, F. E., Cossey, V., Debaveye, Y. A., Janssens, C., CALSTER, B. V., & Wynants, L. (2024) · Research Square · doi:10.21203/rs.3.rs-4384973/v1 · Cited by 5 · see citing works
Wang, X., Bampton, T. J., Dharmaprani, D., Mahajan, R., & Palmer, L. J. (2025) · BMJ Digital Health & AI · doi:10.1136/bmjdhai-2025-000154 · Cited by 4 · see citing works
Feng, Z., Zheng, L., & Liu, D. (2025) · Analytical Methods · doi:10.1039/d5ay01554e · Cited by 4 · see citing works
Mohtasham, F., Nazari, S. S. H., Pourhoseingholi, M. A., Kavousi, K., & Zali, M. R. (2026) · PLoS ONE · doi:10.1371/journal.pone.0341198 · Cited by 4 · see citing works
- Authors
- Van Calster, B., Steyerberg, E. W., Wynants, L., & van Smeden, M.
- Affiliations
- KU Leuven (BE)
- Maastricht University (NL)
- Venue
- BMC Medicine
- Published
- 2023-02-24
- Volume
- 21
- Issue
- 1
- Pages
- 70-70
- ISSN
- 1741-7015 (ISSN-L)
- PMC
- PMC9951847
- OpenAlex
- W4321782827
- Cited by
- 286
- References
- 56
- Citations by year
- 2026: 99 · 2025: 91 · 2024: 67 · 2023: 29
- FWCI
- 58.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- Leiden Repository (Leiden University) (submittedVersion, cc-by)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- Fonds Wetenschappelijk Onderzoek (G097322N)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- clinical prediction model, model validation, external validation, internal validation, model updating, clinical decision support
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.
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 ›
Are you an author, or do you see a problem with this page? Request a review
