The Medical Segmentation Decathlon
Open accessAbstract
International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.
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?
MSD-Colon contains 126 abdominal CT volumes with colon tumor annotations, and KiTS21 contains 300 CT volumes with kidney tumor annotations.
“MSD-Colon contains 126 abdominal CT volumes with colon tumor annotations, and KiTS21 contains 300 CT volumes with kidney tumor annotations.”
From Correction-aware interactive 3D tumor segmentation with sparse and revisable prompts (2026) · cites this paper as [2]
Table 2 (Methods, The Decathlon mission; page unavailable) lists the Colon task as 190 CT volumes targeting colon cancer primaries, split into 126 training and 64 test volumes. Thus 126 is not the total stated in the claim. The paper’s dataset summary does not address KiTS21 or its claimed 300 kidney-tumor CT volumes. (LLM, conf=0.90)
Experimental — accuracy not yet measured
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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Self-citationAdam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
The Liver Tumor Segmentation Benchmark (LiTS)
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102680 · Cited by 1,412 · see citing works
Self-citationDeep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., & van der Laak, J. (2016) · Scientific Reports · doi:10.1038/srep26286 · Cited by 1,069 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 37
- [2]Poplin R Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning Nat. Biomed. Eng. 2018 2 158 164 10.1038/s41551-018-0195-0 31015713 · DOI · Paper page
- [3]Stokes JM A deep learning approach to antibiotic discovery Cell 2020 180 688 702 10.1016/j.cell.2020.01.021 32084340 PMC8349178 · DOI · Paper page
- [4]Ayache N Duncan J 20th anniversary of the medical image analysis journal (media) Med. Image Anal. 2016 33 1 3 10.1016/j.media.2016.07.004 27503079 · DOI
- [5]Liang S Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning Eur. Radiol. 2019 29 1961 1967 10.1007/s00330-018-5748-9 30302589 · DOI
- [6]Assefa D Robust texture features for response monitoring of glioblastoma multiforme on-weighted and-flair mr images: A preliminary investigation in terms of identification and segmentation Med. Phys. 2010 37 1722 1736 10.1118/1.3357289 20443493 · DOI
- [7]Maier-Hein, L et al. Why rankings of biomedical image analysis competitions should be interpreted with care. Nat. Commun.9. 10.1038/s41467-018-07619-7 (2018). PMC6284017 30523263 · DOI
- [8]Isensee F Jaeger PF Kohl SA Petersen J Maier-Hein KH nnu-net: a self-configuring method for deep learning-based biomedical image segmentation Nat. Commun. 2021 18 203 211 33288961
- [9]Dice LR Measures of the amount of ecologic association between species Ecology 1945 26 297 302 10.2307/1932409 · DOI
- [10]Nikolov, S. et al. Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study. J Med Internet Res.23, e26151 (2021). PMC8314151 34255661
- [13]Zhang, T. Solving large scale linear prediction problems using stochastic gradient descent algorithms, In Proc. Twenty-First International Conference on Machine Learning. 116 (Association for Computing Machinery, 2004).
- [14]Xia, Y. et al. 3d semi-supervised learning with uncertainty-aware multi-view co-training, In Proc. IEEE Winter Conference on Applications of Computer Vision, 3646–3655 (IEEE Computer Society, 2020).
- [16]He X Zhao K Chu X Automl: a survey of the state-of-the-art Knowl. Based Syst. 2021 212 106622 10.1016/j.knosys.2020.106622 · DOI
- [17]Wiesenfarth M Methods and open-source toolkit for analyzing and visualizing challenge results Sci. Rep. 2021 11 1 15 10.1038/s41598-020-79139-8 33504883 PMC7841186 · DOI
- [18]Kendall MG A new measure of rank correlation Biometrika 1938 30 81 93 10.1093/biomet/30.1-2.81 · DOI
- [19]Ma, J. Cutting-edge 3d medical image segmentation methods in 2020: Are happy families all alike? arXiv Preprint at https://arxiv.org/abs/2101.00232 (2021). · arXiv
- [20]Reinke, A. et al. How to exploit weaknesses in biomedical challenge design and organization, In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention, 388–395 (Springer, 2018).
- [21]Joskowicz L Cohen D Caplan N Sosna J Inter-observer variability of manual contour delineation of structures in ct Eur. Radiol. 2019 29 1391 1399 10.1007/s00330-018-5695-5 30194472 · DOI
- [22]Maier-Hein L Bias: transparent reporting of biomedical image analysis challenges Med. Image Anal. 2020 66 101796 10.1016/j.media.2020.101796 32911207 PMC7441980 · DOI · Paper page
- [23]Reinke, A. et al. Common limitations of image processing metrics: A picture story. arXiv Preprint at https://arxiv.org/abs/2104.05642 (2021). · arXiv
- [25]Codella, N.C.F et al. Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBN), hosted by the international skin imaging collaboration (ISIC). In Proc. IEEE 15th International Symposium on Biomedical Imaging, 168–172 (ISBI 2018).
- [26]Ross T Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge Med. Image Anal. 2021 70 101920 10.1016/j.media.2020.101920 33676097 · DOI
- [27]Campello, V. M. et al. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge. IEEE Transactions on Medical Imaging. 40, 3543–3554 (IEEE, 2021). 34138702
- [28]Campadelli P Casiraghi E Esposito A Liver segmentation from computed tomography scans: a survey and a new algorithm Artif. Intel. Med. 2009 45 185 196 10.1016/j.artmed.2008.07.020 19059767 · DOI
- [29]Sirinukunwattana K Gland segmentation in colon histology images: the glas challenge contest Med. Image Anal. 2017 35 489 502 10.1016/j.media.2016.08.008 27614792 · DOI
- [30]Re TJ Enhancing pancreatic adenocarcinoma delineation in diffusion derived intravoxel incoherent motion f-maps through automatic vessel and duct segmentation Magn. Reson. Med. 2011 66 1327 1332 10.1002/mrm.22931 21437979 · DOI
- [31]Bello, I. et al. Revisiting resnets: Improved training and scaling strategies. arXiv Preprint at https://arxiv.org/abs/2103.07579 (2021). · arXiv
- [33]He, Y., Yang, D., Roth, H., Zhao, C. & Xu, D. Dints: differentiable neural network topology search for 3d medical image segmentation. CoRR abs/2103.15954. http://arxiv.org/abs/2103.15954 (2021). · arXiv
- [34]Maier-Hein L Why rankings of biomedical image analysis competitions should be interpreted with care Nat. Commun. 2018 9 5217 10.1038/s41467-018-07619-7 30523263 PMC6284017 · DOI
- [35]Simpson, A.L. et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv e-prints http://arxiv.org/abs/1902.09063 (2019). · arXiv
- [36]Menze BH The multimodal brain tumor image segmentation benchmark (brats) IEEE Trans. Med. Imag. 2015 34 1993 2024 10.1109/TMI.2014.2377694 PMC4833122 25494501 · DOI · Paper page
- [37]Bakas S Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features Sci. Data 2017 4 1 13 10.1038/sdata.2017.117 PMC5685212 28872634 · DOI · Paper page
- [38]Bakas, S. et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv Preprint at https://arxiv.org/abs/1811.02629 (2018b). · arXiv
- [39]Tobon-Gomez C Benchmark for algorithms segmenting the left atrium from 3d ct and mri datasets IEEE Trans. Med. Imag. 2015 34 1460 1473 10.1109/TMI.2015.2398818 25667349 · DOI
- [41]Bakas, S. et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. CoRR abs/1811.02629. http://arxiv.org/abs/1811.02629 (2018a). · arXiv
- [43]Milletari, F., Navab, N. & Ahmadi, S.-A. V-net: Fully convolutional neural networks for volumetric medical image segmentation, In Proc. Fourth International Conference on 3D Vision (3DV), 565–571. (IEEE, 2016).
- [44]Roy, A.G., Conjeti, S., Navab, N. & Wachinger, C. Quicknat: Segmenting MRI neuroanatomy in 20 seconds. CoRR abs/1801.04161. http://arxiv.org/abs/1801.04161 (2018). · arXiv
- [45]Kamnitsas, K. et al. Deepmedic for brain tumor segmentation. In Proc. International workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 138–149 (Springer, 2016).
Cited by
Sorted by citations
Segment anything in medical images
Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2024) · Nature Communications · doi:10.1038/s41467-024-44824-z · Cited by 2,913 · see citing works
“However, a significant limitation of many current medical image segmentation models is their task-specific nature.”
The Liver Tumor Segmentation Benchmark (LiTS)
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102680 · Cited by 1,412 · see citing works
Self-citationMedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification
Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., Pfister, H., & Ni, B. (2023) · Scientific Data · doi:10.1038/s41597-022-01721-8 · Cited by 1,043 · see citing works
“As reported in the winning solutions 6,7 , hyperparameter tuning, pre/post-processing, model ensemble strategies and training/test-time augmentation could be more important than the machine learning part ( e.g. , model architectures, learning scheme).”
BackgroundSegment anything model for medical images?
Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103061 · Cited by 536 · see citing works
3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation
Gong, S., Zhong, Y., Ma, W., Li, J., Wang, Z., Zhang, J., Heng, P.-A., & Dou, Q. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103324 · Cited by 156 · see citing works
Federated learning for healthcare applications
Chaddad, A., Wu, Y., & Desrosiers, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3325822 · Cited by 136 · see citing works
Sunoqrot, M. R. S., Saha, A., Hosseinzadeh, M., Elschot, M., & Huisman, H. (2022) · European Radiology Experimental · doi:10.1186/s41747-022-00288-8 · Cited by 76 · see citing works
Self-citationSegment Anything in Medical Images
Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2023) · arXiv · doi:10.48550/arxiv.2304.12306 · Cited by 67 · see citing works
Towards foundation models of biological image segmentation
Ma, J., & Wang, B. (2023) · Nature Methods · doi:10.1038/s41592-023-01885-0 · Cited by 57 · see citing works
PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
Zhang, X., Wu, C., Zhao, Z., Lin, W., Zhang, Y., Wang, Y., & Xie, W. (2023) · arXiv · doi:10.48550/arxiv.2305.10415 · Cited by 66 · see citing works
“Modality Source Images QA pairs VQA-RAD [18] Radiology MedPix ® database 0.3k 3.5k PathVQA [12] Pathology PEIR Digital Library [14] 5k 32.8k SLAKE [23] Radiology MSD [3], ChestX-ray8 [36], CHAOS [15] 0.7k 14k VQA-Med-2021 [5] Radiology MedPix ® database 5k 5k PMC-VQA Mixture * PubMed Central ® 149k 227k Figure 1: The…”
Self-citationThe impact of ChatGPT and LLMs on medical imaging stakeholders: Perspectives and use cases
Yang, J., Li, H. B., & Wei, D. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100007 · Cited by 46 · see citing works
Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., et al. (2023) · Radiographics · doi:10.1148/rg.230180 · Cited by 35 · see citing works
Self-citationvesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation
Wittmann, B., Wattenberg, Y., Amiranashvili, T., Shit, S., & Menze, B. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.01944 · Cited by 25 · see citing works
Self-citationSzentimrey, Z., Ameri, G., Hong, C. X., Cheung, R. Y. K., Ukwatta, E., & Eltahawi, A. (2023) · Medical Physics · doi:10.1002/mp.16389 · Cited by 17 · see citing works
A deep learning-based interactive medical image segmentation framework with sequential memory
Mikhailov, I., Chauveau, B., Bourdel, N., & Bartoli, A. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108038 · Cited by 15 · see citing works
PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts
Li, H., Liu, H., Hu, D., Wang, J., & Oguz, I. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72384-1_37 · Cited by 12 · see citing works
Correction-aware interactive 3D tumor segmentation with sparse and revisable prompts
Li, H., & Li, H. (2026) · The Visual Computer · doi:10.1007/s00371-026-04560-5
“Public datasets and challenges have supported systematic comparison across organs and tumors, including the Medical Segmentation Decathlon [2] and KiTS21 [8].”
Kanber, B. M., Gou, S., Liu, B., Noaman, N. F., Mukred, M., & Al Smadi, A. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134426
Conformal Lesion Segmentation for 3D Medical Images
Tan, B., Wang, Z., Duan, J., Xu, K., Shen, H. T., Shi, X., & Shen, F. (2025) · arXiv · doi:10.48550/arxiv.2510.17897
- Authors
- Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., van Ginneken, B., Bilello, M., Bilic, P., Christ, P. F., Do, R. K. G., Gollub, M. J., Heckers, S. H., Huisman, H., Jarnagin, W. R., McHugo, M. K., Napel, S., Pernicka, J. S. G., Rhode, K., Tobon-Gomez, C., Vorontsov, E., Meakin, J. A., Ourselin, S., Wiesenfarth, M., Arbeláez, P., Bae, B., Chen, S., Daza, L., Feng, J., He, B., Isensee, F., Ji, Y., Jia, F., Kim, I., Maier-Hein, K., Merhof, D., Pai, A., Park, B., Perslev, M., Rezaiifar, R., Rippel, O., Sarasua, I., Shen, W., Son, J., Wachinger, C., Wang, L., Wang, Y., Xia, Y., Xu, D., Xu, Z., Zheng, Y., Simpson, A. L., Maier-Hein, L., & Cardoso, M. J.
- Affiliations
- King's College London (GB)
- German Cancer Research Center (DE)
- University of Pennsylvania (US)
- National Institutes of Health (US)
- Vanderbilt University (US)
- Radboud University Medical Center (NL)
- University of Zurich (CH)
- Google DeepMind (United Kingdom) (GB)
- Technical University of Munich (DE)
- Memorial Sloan Kettering Cancer Center (US)
- Vanderbilt University Medical Center (US)
- Stanford Medicine (US)
- Polytechnique Montréal (CA)
- Universidad de Los Andes (CO)
- Tencent (China) (CN)
- Tsinghua University (CN)
- Chinese Academy of Sciences (CN)
- Xiamen University (CN)
- Kakao Brain (South Korea) (KR)
- University Hospital Heidelberg (DE)
- Fraunhofer Institute for Digital Medicine (DE)
- University of Copenhagen (DK)
- RWTH Aachen University (DE)
- Shanghai Jiao Tong University (CN)
- LMU Klinikum (DE)
- East China Normal University (CN)
- Johns Hopkins University (US)
- Nvidia (United States) (US)
- Queen's University (CA)
- Corresponding author
- Antonelli, Michela
- ORCID
- Antonelli, Michela
- Reinke, Annika
- Bakas, Spyridon
- Farahani, Keyvan
- Kopp-Schneider, Annette
- Landman, Bennett A.
- Litjens, Geert
- Menze, Bjoern
- Ronneberger, Olaf
- Summers, Ronald M.
- Bilello, Michel
- Do, Richard K. G.
- Gollub, Marc J.
- Heckers, Stephan H.
- Huisman, Henkjan
- Jarnagin, William R.
- McHugo, Maureen K.
- Napel, Sandy
- Pernicka, Jennifer S. Golia
- Rhode, Kawal
- Vorontsov, Eugene
- Meakin, James A.
- Wiesenfarth, Manuel
- Arbeláez, Pablo
- Bae, Byeonguk
- Chen, Sihong
- Daza, Laura
- Feng, Jianjiang
- He, Baochun
- Isensee, Fabian
- Ji, Yuanfeng
- Jia, Fucang
- Maier-Hein, Klaus
- Merhof, Dorit
- Pai, Akshay
- Park, Beomhee
- Perslev, Mathias
- Rippel, Oliver
- Shen, Wei
- Son, Jaemin
- Wachinger, Christian
- Wang, Liansheng
- Xia, Yingda
- Xu, Daguang
- Xu, Zhanwei
- Zheng, Yefeng
- Simpson, Amber L.
- Cardoso, M. Jorge
- Venue
- Nature Communications
- Published
- 2022-07-15
- Volume
- 13
- Issue
- 1
- Pages
- 4128-4128
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC9287542
- OpenAlex
- W3172681723
- Cited by
- 1,384
- References
- 5
- Citations by year
- 2026: 299 · 2025: 403 · 2024: 344 · 2023: 230 · 2022: 86 · 2021: 16 · 2020: 1 · 2019: 2
- FWCI
- 87.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- IRIS eCampus Telematic University (Università degli Studi eCampus) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- mediaTUM (Technical University of Munich) (submittedVersion, other-oa)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Zurich Open Repository and Archive (University of Zurich)
- Greater South Information System
- Funders
- Wellcome Trust (WT213038, WT203148)
- UK Research and Innovation
- National Institute for Health and Care Research
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- KWF Kankerbestrijding
- Siemens Healthineers
- National Institutes of Health (1s10od023495-01, NINDS:R01NS042645, NCI:U24CA189523)
- Engineering and Physical Sciences Research Council
- National Cancer Institute (U24CA189523, U01CA242871, P30CA008748)
- National Institute of Neurological Disorders and Stroke (R01NS042645, NINDS:R01NS042645)
- NIH Clinical Center
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- Medical Segmentation Decathlon, medical image segmentation, benchmarking, cross-task generalization, unseen tasks, multimodal medical imaging, AI training
- MeSH terms
- Algorithms, Image Processing, Computer-Assisted
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
