National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation
Open accessAbstract
PURPOSE: Artificial intelligence (AI) is about to touch every aspect of radiation therapy, from consultation to treatment planning, quality assurance, therapy delivery, and outcomes modeling. There is an urgent need to train radiation oncologists and medical physicists in data science to help shepherd AI solutions into clinical practice. Poorly trained personnel may do more harm than good when attempting to apply rapidly developing and complex technologies. As the amount of AI research expands in our field, the radiation oncology community needs to discuss how to educate future generations in this area. METHODS AND MATERIALS: The National Cancer Institute (NCI) Workshop on AI in Radiation Oncology (Shady Grove, MD, April 4-5, 2019) was the first of 2 data science workshops in radiation oncology hosted by the NCI in 2019. During this workshop, the Training and Education Working Group was formed by volunteers among the invited attendees. Its members represent radiation oncology, medical physics, radiology, computer science, industry, and the NCI. RESULTS: In this perspective article written by members of the Training and Education Working Group, we provide and discuss action points relevant for future trainees interested in radiation oncology AI: (1) creating AI awareness and responsible conduct; (2) implementing a practical didactic curriculum; (3) creating a publicly available database of training resources; and (4) accelerating learning and funding opportunities. CONCLUSION: Together, these action points can facilitate the translation of AI into clinical practice.
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?
Among the 6 articles mentioning plans for curricular change, there is no indication of published articles following up on those plans.
“Of the 6 articles mentioning plans for curricular change, there is no indication of published articles following up on these plans.38,44,50,51”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [38]
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
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856 · see citing works
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · see citing works
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381 · see citing works
Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002683 · Cited by 1,719 · see citing works
AI can be sexist and racist — it’s time to make it fair
Zou, J., & Schiebinger, L. (2018) · Nature · doi:10.1038/d41586-018-05707-8 · Cited by 765 · see citing works
Self-citationPredicting outcomes in radiation oncology—multifactorial decision support systems
Lambin, P., van Stiphout, R. G. P. M., Starmans, M. H. W., Velazquez, E. R., Nalbantov, G. I., Aerts, H. J. W. L., Roelofs, E., van Elmpt, W., Boutros, P. C., Granone, P. M., et al. (2012) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2012.196 · Cited by 396 · see citing works
Distributed deep learning networks among institutions for medical imaging
Chang, K., Balachandar, N., Lam, C., Yi, D., Brown, J., Beers, A., Rosen, B., Rubin, D. L., & Kalpathy-Cramer, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy017 · Cited by 340 · see citing works
Big Data and machine learning in radiation oncology: State of the art and future prospects
Bibault, J.-E., Giraud, P., & Burgun, A. (2016) · Cancer Letters · doi:10.1016/j.canlet.2016.05.033 · Cited by 298 · see citing works
Margolis, R., Derr, L. K., Dunn, M. C., Huerta, M. F., Larkin, J. E., Sheehan, J. R., Guyer, M. S., & Green, E. D. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-002974 · Cited by 276 · see citing works
Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?
Thompson, R. F., Valdés, G., Fuller, C. D., Carpenter, C. M., Morin, O., Aneja, S., Lindsay, W. D., Aerts, H. J. W. L., Agrimson, B., Deville, C., et al. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.05.030 · Cited by 260 · see citing works
Self-citationMachine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective
Kang, J., Schwartz, R. S., Flíckinger, J. C., & Beriwal, S. (2015) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2015.07.2286 · Cited by 215 · see citing works
Self-citationMayo, C. S., Moran, J. M., Bosch, W. R., Xiao, Y., McNutt, T., Popple, R. A., Michalski, J. M., Feng, M. U.-S., Marks, L. B., Fuller, C. D., et al. (2017) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2017.12.013 · Cited by 213 · see citing works
Jochems, A., Deist, T. M., Naqa, I. E., Kessler, M. L., Mayo, C., Reeves, J., Jolly, S., Matuszak, M. M., Haken, R. K. T., van Soest, J., et al. (2017) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2017.04.021 · Cited by 150 · see citing works
Establishment of a Radiogenomics Consortium
West, C., & Rosenstein, B. S. (2010) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2009.12.017 · Cited by 141 · see citing works
Collado‐Mesa, F., Alvarez, E., & Arheart, K. L. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.12.021 · Cited by 136 · see citing works
Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs
Feng, M. U.-S., Valdés, G., Dixit, N., & Solberg, T. D. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00110 · Cited by 112 · see citing works
Lee, S., Kerns, S. L., D, H. O. M., Rosenstein, B. S., Deasy, J. O., & Oh, J. H. (2018) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.01.054 · Cited by 93 · see citing works
Grossberg, A. J., Mohamed, A., Elhalawani, H. M., Bennett, W., Smith, K. E., Nolan, T. S., Williams, B., Chamchod, S., Heukelom, J., Kantor, M., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.173 · Cited by 93 · see citing works
Machine Learning and Radiogenomics: Lessons Learned and Future Directions
Kang, J., Rancati, T., Lee, S., Oh, J. H., Kerns, S. L., Scott, J. G., Schwartz, R. S., Kim, S. Y., & Rosenstein, B. S. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00228 · Cited by 73 · see citing works
Self-citationInterpretation of Neural Networks is FragilePreprint
Ghorbani, A., Abid, A., & Zou, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.10547 · Cited by 72 · see citing works
Self-citationArtificial intelligence in radiology: friend or foe? Where are we now and where are we heading?
Pakdemirli, E. (2019) · Acta Radiologica Open · doi:10.1177/2058460119830222 · Cited by 72 · see citing works
Mak, R. H., Endres, M. G., Paik, J. H., Sergeev, R. A., Aerts, H. J. W. L., Williams, C. L., Lakhani, K. R., & Guinan, E. C. (2019) · JAMA Oncology · doi:10.1001/jamaoncol.2019.0159 · Cited by 67 · see citing works
Oh, J. H., Kerns, S. L., D, H. O. M., Powell, S. N., Rosenstein, B. S., & Deasy, J. O. (2017) · Scientific Reports · doi:10.1038/srep43381 · Cited by 48 · see citing works
Gillespie, E. F., Panjwani, N., Golden, D. W., Gunther, J. R., Chapman, T. R., Brower, J. V., Kosztyla, R., Larson, G. M., Neppala, P., Moiseenko, V., et al. (2016) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2016.11.050 · Cited by 41 · see citing works
Elhalawani, H. M., Lin, T. A., Volpe, S., Mohamed, A., White, A. L., Zafereo, J., Wong, A. J., Berends, J. E., Abohashem, S., Williams, B., et al. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00294 · Cited by 38 · see citing works
Big Data Analytics for Prostate Radiotherapy
Coates, J. T. T., Souhami, L., & Naqa, I. E. (2016) · Frontiers in Oncology · doi:10.3389/fonc.2016.00149 · Cited by 36 · see citing works
Thomas, G., Eisenhauer, E. A., Bristow, R. G., Grau, C., Hurkmans, C., Ost, P., Gückenberger, M., Deutsch, É., Lacombe, D., & Weber, D. C. (2020) · European Journal of Cancer · doi:10.1016/j.ejca.2020.02.050 · Cited by 35 · see citing works
Genomics models in radiotherapy: From mechanistic to machine learning
Kang, J., Coates, J. T. T., Strawderman, R. L., Rosenstein, B. S., & Kerns, S. L. (2020) · Medical Physics · doi:10.1002/mp.13751 · Cited by 28 · see citing works
Self-citationArtificial Intelligence in Radiation Oncology Imaging
Thompson, R. F., Valdés, G., Fuller, C. D., Carpenter, C. M., Morin, O., Aneja, S., Lindsay, W. D., Aerts, H. J. W. L., Agrimson, B., Deville, C., et al. (2018) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.05.070 · Cited by 26 · see citing works
Self-citationA Call for Change in the ABR Initial Certification Examination in Radiation Oncology
Lee, W. R., & Amdur, R. J. (2019) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.12.046 · Cited by 23 · see citing works
The Challenge of Regulating Clinical Decision Support Software After 21st Century Cures
Evans, B. J., & Ossorio, P. N. (2018) · American Journal of Law & Medicine · doi:10.1177/0098858818789418 · Cited by 21 · see citing works
Wallner, P. E., Kachnic, L. A., Alektiar, K. M., Davis, B. J., Hardenbergh, P. H., & Ng, A. K. (2019) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2019.01.090 · Cited by 13 · see citing works
Mouw, K. W., Beck, T., Keen, J. C., & Dicker, A. P. (2018) · JCO Clinical Cancer Informatics · doi:10.1200/cci.18.00045 · Cited by 7 · see citing works
Mackie, T., Jackson, E. F., & Giger, M. L. (2018) · Medical Physics · doi:10.1002/mp.13135 · Cited by 6 · see citing works
Career Enrichment Opportunities at the Scientific Frontier in Radiation Oncology
Thompson, R. F., Fuller, C. D., Berman, A. T., Aneja, S., & Thomas, C. R. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.18.00126 · Cited by 4 · see citing works
Self-citationKang, J., Thompson, R. F., Fuller, C. D., Camphausen, K. A., Gabriel, P. E., & Thomas, C. R. (2019) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2019.10.044 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 9
- [1]American Medical Association AMA adopt policy, integrate augmented intelligence in physician training Available at: https://www.ama-assn.org/press-center/press-releases/ama-adopt-policy-integrate-augmented-intelligence-physician-training
- [16]National Cancer Institute NCI convenes workshop on artificial intelligence (AI) in radiation oncology Available at: https://dctd.cancer.gov/NewsEvents/20190523_ai_in_radiation_oncology.htm
- [27]Caruana R. Lou Y. Gehrke J. Koch P. Sturm M. Elhadad N. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2015 KDD New York 1721 1730
- [30]AMIA AMIA urges more work on FDA’s decision support guidance Available at: https://www.amia.org/news-and-publications/press-release/amia-urges-more-work-fda%E2%80%99s-decision-support-guidance
- [33]Patient-led data sharing — a new paradigm for electronic health data Available at: https://catalyst.nejm.org/patient-led-health-data-paradigm/
- [52]MICCAI challenges Available at: http://www.miccai.org/events/challenges
- [53]Methods in Clinical Cancer Research Workshop Available at: https://www.esmo.org/meetings/workshops-courses/methods-in-clinical-cancer-research-mccr
- [54]AACR Molecular Biology in Clinical Oncology Workshop Available at: https://www.aacr.org/meeting/mbco-2020/
- [56]Jain A.K. Berny-Lang M. State of Data Science in Radiation Oncology Workshop Available at: https://events.cancer.gov/ccr/row
Cited by
Sorted by citations
Artificial intelligence education programs for health care professionals: Scoping review
Charow, R., Jeyakumar, T., Younus, S., Dolatabadi, E., Salhia, M., Al-Mouaswas, D., Anderson, M., Balakumar, S., Clare, M., Dhalla, A., et al. (2021) · JMIR Medical Education · doi:10.2196/31043 · Cited by 291 · see citing works
Fountzilas, E., Pearce, T. E., Baysal, M. A., Chakraborty, A., & Tsimberidou, A. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01471-y · Cited by 219 · see citing works
Kurniawan, M. H., Handiyani, H., Nuraini, T., Hariyati, R. T. S., & Sutrisno, S. (2024) · Annals of Medicine · doi:10.1080/07853890.2024.2302980 · Cited by 198 · see citing works
Grunhut, J., Wyatt, A. T., & Marques, O. (2021) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205211036836 · Cited by 161 · see citing works
“38,44,50,51 It is not fully understood what is preventing medical schools from implementing these changes.”
BackgroundNeeds, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum
Grunhut, J., Marques, O., & Wyatt, A. T. M. (2022) · JMIR Medical Education · doi:10.2196/35587 · Cited by 166 · see citing works
Bin Sawad, A., Narayan, B., Alnefaie, A., Maqbool, A., Mckie, I., Smith, J., Yuksel, B., Puthal, D., Prasad, M., & Kocaballi, A. B. (2022) · Sensors · doi:10.3390/s22072625 · Cited by 113 · see citing works
Hurkmans, C., Bibault, J.-E., Brock, K. K., van Elmpt, W., Feng, M., David Fuller, C., Jereczek-Fossa, B. A., Korreman, S., Landry, G., Madesta, F., et al. (2024) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2024.110345 · Cited by 110 · see citing works
Anagnostou, M., Karvounidou, O., Katritzidaki, C., Kechagia, C., Melidou, K., Mpeza, E., Konstantinidis, I., Kapantai, E., Berberidis, C., Magnisalis, I., et al. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09634-1 · Cited by 58 · see citing works
Artificial intelligence in radiology: Are Saudi residents ready, prepared, and knowledgeable?
Khafaji, M. A., Safhi, M. A., Albadawi, R. H., Al-Amoudi, S. O., Shehata, S. S., & Toonsi, F. (2022) · Saudi Medical Journal · doi:10.15537/smj.2022.43.1.20210337 · Cited by 48 · see citing works
Ma, X., Zhang, Q., He, L., Liu, X., Xiao, Y., Hu, J., Cai, S., Cai, H., & Yu, B. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1487676 · Cited by 35 · see citing works
DeJohn, C. R., Grant, S. R., & Seshadri, M. (2022) · Cancers · doi:10.3390/cancers14030665 · Cited by 21 · see citing works
Moore, N., McWilliam, A., & Aneja, S. (2022) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2022.10.009 · Cited by 17 · see citing works
Self-citationArtificial intelligence across oncology specialties: current applications and emerging tools
Kang, J., Lafata, K. J., Kim, E., Yao, C. M. K. L., Lin, F. P. Y., Rattay, T., Nori, H., Katsoulakis, E., & Lee, C. I. (2024) · BMJ Oncology · doi:10.1136/bmjonc-2023-000134 · Cited by 14 · see citing works
Self-citationGenerative artificial intelligence in oncology
Ganjavi, C., Melamed, S., Biedermann, B. M., Eppler, M., Rodler, S., Layne, E., Cei, F., Gill, I. S., & Cacciamani, G. E. (2025) · Current Opinion in Urology · doi:10.1097/mou.0000000000001272 · Cited by 10 · see citing works
Arif, W. M. (2024) · International Journal of General Medicine · doi:10.2147/ijgm.s465944 · Cited by 9 · see citing works
A roadmap for research in medical physics via academic medical centers: The DIVERT Model
Pogue, B. W., Zhang, R., & Gladstone, D. J. (2021) · Medical Physics · doi:10.1002/mp.14849 · Cited by 8 · see citing works
Understanding and Use of Artificial Intelligence Among Doctors in a University Hospital in Morocco
Berrami, H., Serhier, Z., Jallal, M., & Othmani, M. B. (2023) · Studies in health technology and informatics · doi:10.3233/shti230781 · Cited by 6 · see citing works
Medical Physics Training and Education: Learning From the Past and Looking to the Future
Bertholet, J., Al‐Hallaq, H., Toma-Daşu, I., Ingledew, P. A., & Carlson, D. J. (2023) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2023.07.039 · Cited by 6 · see citing works
Wen, F., Chen, Z., Wang, X., Dou, M., Yang, J., Yao, Y., & Shen, Y. (2024) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.14482 · Cited by 5 · see citing works
Purkayastha, S., Shalu, H., Gutman, D. A., Holodny, A. I., Modak, S. I., Basu, E. M., Kushner, B. H., Kramer, K., Haque, S. S., & Stember, J. N. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01165-z · Cited by 5 · see citing works
Provider Engagement in Radiation Oncology Data Science: Workshop Report
Jain, A. K., Aneja, S., Fuller, C. D., Dicker, A. P., Chung, C. D., Kim, E., Kirby, J., Quon, H., Lam, C. J. K., Louv, W. C., et al. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00051 · Cited by 4 · see citing works
Self-citationGlobal Workforce and Access: Demand, Education, Quality
Grover, S., Court, L. E., Amoo-Mitchual, S., Longo, J. M., Rodin, D. L., Scott, A. A., Lievens, Y. N., Yap, M. L., Abdel–Wahab, M., Lee, P., et al. (2024) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2024.07.003 · Cited by 3 · see citing works
Education as a Tool to Navigate Changing Technology in Radiation Oncology
Gillan, C., Low, D. A., & Raman, S. (2026) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2025.150998 · Cited by 1 · see citing works
Empowering Healthcare Professionals Through AI-Powered Lifelong Learning for Improving Patient Care
Rajendran, R., Subramanian, Y. R., Poddar, S., & Geetha, V. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2440-0.ch006 · Cited by 1 · see citing works
Saraf, A., Boyd, G. H., Leo, A. D., Gawu, P., Pinnix, C. C., Braunstein, S. E., Jimenez, R., Franco, I. “., & Singer, L. (2023) · Applied Radiation Oncology · doi:10.37549/aro1334
Gharia, B. K., Suhagia, B. N., & Jain, V. K. (2023) · International Journal of Pharmaceutical Chemistry and Analysis · doi:10.18231/j.ijpca.2023.045
Kim, E., Fuller, C. D., Thomas, C. R., Jain, A., & Thompson, R. F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-822000-9.00002-1
Self-citationGoossens, E., Kemajou, P. M., Coorevits, P. L., Probst, S., & Beeckman, D. (2026) · BMJ Open · doi:10.1136/bmjopen-2025-113547
Optimizing the delivery of radiotherapy with artificial intelligence
Katsoulakis, E., & Naqa, I. E. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-026-01204-4
- Authors
- Kang, J., Thompson, R. F., Aneja, S., Lehman, C., Trister, A., Zou, J., Obcemea, C., & El Naqa, I.
- Affiliations
- University of Rochester Medicine (US)
- Oregon Health & Science University (US)
- Yale University (US)
- Harvard University (US)
- Gates Foundation (US)
- Chan Zuckerberg Biohub San Francisco (US)
- National Cancer Institute (US)
- Corresponding author
- Kang, John
- Venue
- Practical Radiation Oncology
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 11
- Issue
- 1
- Pages
- 74-83
- ISSN
- 1879-8500 (ISSN-L)
- PMC
- PMC7293478
- OpenAlex
- W2982643860
- Cited by
- 30
- References
- 33
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 10 · 2023: 4 · 2022: 6 · 2021: 4 · 2020: 1
- FWCI
- 1.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Practical Radiation Oncology (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Agency for Healthcare Research and Quality
- American Cancer Society
- National Science Foundation
- RSNA Research and Education Foundation
- National Cancer Institute
- National Institute on Aging
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advances in Oncology and Radiotherapy
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- artificial intelligence, radiation oncology, medical physics, data science education, responsible conduct of research, AI awareness, training materials, clinical translation of AI, National Cancer Institute
- MeSH terms
- Radiation Oncologists, Artificial Intelligence, Curriculum, Humans, Neoplasms, United States, Radiation Oncology, National Cancer Institute (U.S.)
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
