The challenges of explainable AI in biomedical data science
Open accessAbstract
With the surge of biomedical data science, more and more AI techniques are employed to discover knowledge, unveil latent data behavior, generate new insight, and seek optimal strategies in decision making.Different AI methods have been proposed and developed in almost all different biomedical data science fields that range from drug discovery, electronic medical records (EMRs) data automation, single-cell RNA sequencing, early disease diagnosis, COVID research, and healthcare analytics.The AI methods and systems also generate a massive amount of data or big data that not only bring unpreceded progress in biomedical fields but also new challenges for AI.One of the key challenges should be the explainability of AI in biomedical data science problem-solving.It refers to that an AI method or system should not only bring good results but also have good interpretability, i.e., let users know why this way is the optimal one rather than the others.The existing AI methods employed in biomedical data science generally lack good explainability and may not create trustworthiness and transparency in usage well.For example, a deep learning model may bring good accuracy in disease diagnosis by analyzing corresponding bioimages, but it can be hard to explain well about the setting of thousands of parameters in the model.It can be possible that some small perturbations of the parameters may generate totally different learning results and challenge the robustness and stability of the deep learning model.Since AI models cannot explain themselves well, it is likely to encounter a high risk to make an incorrect decision making and decrease its trustworthiness and reliability, even if it has the advantage in accuracy, speed, or complicate data relationship revealing.On the other hand, the AI interpretation issue has been raised almost ten years ago in some subfield of biomedical data science such as bioinformatics.For instance, bioinformaticians found that the gene markers or network markers recommended from an AI disease diagnosis system may not explain themselves, i.e., the identified markers not only cannot apply themselves well in clinical practice, but also those markers that do well in the clinical practice may not be recommended from the AI system [1].
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?
Studies show the feasibility of reidentifying anonymized data.
“Studies showing the feasibility of reidentifying anonymized data highlight the need for advanced data protection and anonymization methods.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [37]
The involvement of major technology corporations like Google, Microsoft, and Apple in healthcare AI, requiring access to vast amounts of patient health data, amplifies privacy issues.
“The involvement of major technology corporations like Google, Microsoft, and Apple in healthcare AI, requiring access to vast amounts of patient health data, further amplifies these privacy issues.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [37]
The deployment of AI in healthcare is marked by challenges like potential errors, biases, and opaque learning algorithms.
“The deployment of AI in healthcare, marked by challenges like potential errors, biases, and opaque learning algorithms, underscores the importance of tailored regulatory systems.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [37]
Private corporations' control of patient health information in AI applications poses significant privacy risks, with instances of inadequate privacy protection.
“Furthermore, private corporations' control of patient health information in AI applications poses significant privacy risks, with instances of inadequate privacy protection calling for stringent regulation and oversight.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [37]
The evolving nature of AI and algorithms introduces risks to the security of health information, even when anonymized.
“The evolving nature of AI and algorithms introduces risks to the security of health information, even when anonymized.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [37]
As AI continues to promise significant improvements in health outcomes, its commercial implementation faces privacy challenges.
“As AI continues to promise significant improvements in health outcomes, its commercial implementation faces privacy challenges.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [37]
Implementing commercial healthcare AI raises legal and ethical issues, including the necessity of patient consent and data protection.
“Implementing commercial healthcare AI raises several legal and ethical issues, including the necessity of patient consent and data protection.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [37]
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
Explainable Artificial Intelligence: a Systematic Review
Vilone, G., & Longo, L. (2020) · arXiv · doi:10.48550/arxiv.2006.00093 · Cited by 269 · see citing works
Overcome Support Vector Machine Diagnosis Overfitting
Han, H., & Jiang, X. (2014) · Cancer Informatics · doi:10.4137/cin.s13875 · Cited by 139 · see citing works
Self-citationQuantifying critical states of complex diseases using single-sample dynamic network biomarkers
Liu, X., Chang, X., Liu, R., Yu, X., Chen, L., & Aihara, K. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005633 · Cited by 114 · see citing works
Self-citationDiagnostic biases in translational bioinformatics
Han, H. (2015) · BMC Medical Genomics · doi:10.1186/s12920-015-0116-y · Cited by 24 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Explainable AI for Healthcare 5.0: Opportunities and Challenges
Saraswat, D., Bhattacharya, P., Verma, A., Prasad, V. K., Tanwar, S., Sharma, G., Bokoro, P. N., & Sharma, R. (2022) · IEEE Access · doi:10.1109/access.2022.3197671 · Cited by 578 · see citing works
Williamson, S. M., & Prybutok, V. (2024) · Applied Sciences · doi:10.3390/app14020675 · Cited by 567 · see citing works
Explainable AI for Bioinformatics: Methods, Tools and Applications
Karim, M. R., Islam, T., Shajalal, M., Beyan, O., Lange, C., Cochez, M., Rebholz‐Schuhmann, D., & Decker, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad236 · Cited by 209 · see citing works
A guide to multi-omics data collection and integration for translational medicine
Athieniti, E., & Spyrou, G. M. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.050 · Cited by 185 · see citing works
Explainable artificial intelligence for drug discovery and development: a comprehensive survey
Alizadehsani, R., Oyelere, S. S., Hussain, S., Jagatheesaperumal, S. K., Calixto, R. R., Rahouti, M., Roshanzamir, M., & De Albuquerque, V. H. C. (2024) · IEEE Access · doi:10.1109/access.2024.3373195 · Cited by 124 · see citing works
Explainable artificial intelligence for omics data: a systematic mapping study
Toussaint, P. A., Leiser, F., Thiebes, S., Schlesner, M., Brors, B., & Sunyaev, A. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad453 · Cited by 103 · see citing works
Enhance explainability of manifold learning
Han, H., Li, W., Wang, J., Qin, G., & Qin, X. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.05.119 · Cited by 83 · see citing works
Self-citationKnowledge Graphs and Explainable AI in Healthcare
Rajabi, E., & Kafaie, S. (2022) · Information · doi:10.3390/info13100459 · Cited by 63 · see citing works
Generalized biological foundation model with unified nucleic acid and protein language
He, Y., Fang, P., Shan, Y., Pan, Y., Wei, Y., Chen, Y., Chen, Y., Liu, Y., Zeng, Z., Zhou, Z. M., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01044-4 · Cited by 59 · see citing works
Estimate the incubation period of coronavirus 2019 (COVID-19)
Men, K., Li, Y., Wang, X., Zhang, G., Hu, J., Gao, Y., Han, A., Liu, W., & Han, H. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106794 · Cited by 44 · see citing works
Self-citationExplainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE
Roy, D. G., Alvi, P. A., & Santosh, K. (2022) · Electronics · doi:10.3390/electronics12010015 · Cited by 40 · see citing works
Brdar, S., Panić, M. N., Matavulj, P., Stanković, M., Bartolić, D., & Šikoparija, B. (2023) · Scientific Reports · doi:10.1038/s41598-023-30064-6 · Cited by 33 · see citing works
Ouhmouk, M., Baichoo, S., & Abık, M. (2025) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2025.101679 · Cited by 29 · see citing works
Interpretable Machine Learning Assessment
Han, H., Wu, Y., Wang, J., & Han, A. (2022) · Neurocomputing · doi:10.1016/j.neucom.2023.126891 · Cited by 29 · see citing works
Self-citationExplainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review
Madanu, R., Abbod, M., Hsiao, F., Chen, W., & Shieh, J.-S. (2022) · Technologies · doi:10.3390/technologies10030074 · Cited by 27 · see citing works
Translational Bioinformatics Applied to the Study of Complex Diseases
Casotti, M. C., Meira, D. D., Alves, L. N. R., Bessa, B. G. D. O., Campanharo, C. V., Vicente, C. R., Aguiar, C. C., Duque, D. D. A., Barbosa, D. G., dos Santos, E. D. V. W., et al. (2023) · Genes · doi:10.3390/genes14020419 · Cited by 25 · see citing works
Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data
Stock, M., Criekinge, W. V., Boeckaerts, D., Taelman, S., Haeverbeke, M. V., Dewulf, P., & Bernard De Baets, C. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012426 · Cited by 20 · see citing works
Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review
Alhamrani, S. Q., Ball, G., El-Sherif, A. A., Ahmed, S. S., Mousa, N. O., Alghorayed, S. A., Alatawi, N. A., Ali, A. M., Alqahtani, F. A., & Gabre, R. M. (2025) · Cells · doi:10.3390/cells14171385 · Cited by 19 · see citing works
Explainable machine learning for high frequency trading dynamics discovery
Han, H., Forrest, J. Y., Wang, J., yuan, S., Han, F., & Li, D. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121286 · Cited by 11 · see citing works
Self-citationExplainable t-SNE for single-cell RNA-seq data analysisPreprint
Han, H., Zhang, T., Li, C., Benton, M. L., Wang, J., & Li, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.12.476084 · Cited by 11 · see citing works
Self-citationExplainable AI for medical applications
Rao, N. M., Sivaraman, J., Pal, K., & Neelapu, B. C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-19073-5.00020-3 · Cited by 8 · see citing works
Pan, L., Li, M., Lei, H., Miao, S., Hu, X., Tang, Z., Chen, W., & Wang, X. (2024) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-024-01818-7 · Cited by 8 · see citing works
Navigating Healthcare Insights: A Bird’s Eye View of Explainability with Knowledge Graphs
Garg, S., Parikh, S. B., & Garg, S. (2023) · IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) · doi:10.1109/aike59827.2023.00016 · Cited by 7 · see citing works
A Meta-model for ADMET Property Prediction AnalysisPreprint
Padi, S., Cardone, A., & Sriram, R. D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.05.570279 · Cited by 7 · see citing works
Mukhopadhyay, S., Ulaganathan, N., Dumpuri, P., & Aich, P. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4690-8_2 · Cited by 6 · see citing works
Bartoli, F., Cavaleri, D., & Crocamo, C. (2025) · ALPHA PSYCHIATRY · doi:10.31083/ap44494 · Cited by 6 · see citing works
Correia, R. B., Rozum, J. C., Cross, L. E., Felag, J., Gallant, M., Guo, Z., Herr, B. W., Min, A., Sánchez-Valle, J., Rocha, D. S., et al. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf012 · Cited by 6 · see citing works
New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-MakingPreprint
Iftimia, N. A., Pandya, R., & Mahoney, F. (2023) · Preprints.org · doi:10.20944/preprints202306.0243.v1 · Cited by 6 · see citing works
Synergistic Evolution: Pioneering Frontiers of Artificial Intelligence and Robotics in Healthcare
Kaur, J. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-61475-0_12 · Cited by 4 · see citing works
Constructing a Clinical Patient Similarity Network of Gastric Cancer
Zhang, R., Liu, Z., Zhu, C., Cai, H., Yin, K., Zhong, F., & Liu, L. (2024) · Bioengineering · doi:10.3390/bioengineering11080808 · Cited by 4 · see citing works
CONCORD: enhancing COVID-19 research with weak-supervision based numerical claim extraction
Shah, D., Shah, K., Jagani, M., Shah, A., & Chaudhury, B. (2024) · Journal of Intelligent Information Systems · doi:10.1007/s10844-024-00885-6 · Cited by 4 · see citing works
Deep learning for platelet transfusion
Li, N., & Down, D. G. (2023) · Blood · doi:10.1182/blood.2023022981 · Cited by 4 · see citing works
Genomic prediction of plant traits by popular machine learning methods
Kozlov, K. N., Bankin, M., Semenova, E. A., & Samsonova, M. G. (2025) · Vavilov Journal of Genetics and Breeding · doi:10.18699/vjgb-25-49 · Cited by 3 · see citing works
Gutiérrez-Mondragón, M. A., Vellido, A., & König, C. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25126572 · Cited by 3 · see citing works
AI-Powered Diagnosis: A New Weapon Against Cancer and Microbial InfectionsPreprint
Sharma, A., Sharma, N., Gagneja, S., Billa, A., Bhardwaj, N., George, A., Capalash, N., & Sharma, P. (2025) · Preprints.org · doi:10.20944/preprints202506.1301.v1 · Cited by 3 · see citing works
Undie, F. A., Kruglova, L. V., Okache, M. O., Undie, V. A., & Aniah, A. (2024) · Journal of Innovative Research · doi:10.54536/jir.v2i3.3450 · Cited by 2 · see citing works
Frequency Domain Cluster Analysis of Human Activity Using Triaxial Accelerometer Data
Jurčić, K., Šeketa, G., & Magjarević, R. (2024) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-61628-0_8 · Cited by 2 · see citing works
Explainable AI: Ethical Frameworks, Bias, and the Necessity for Benchmarks
Verhoeven, R., Bouisaghouane, W., & Hulscher, J. B. F. (2025) · European Journal of Pediatric Surgery · doi:10.1055/a-2702-1843 · Cited by 2 · see citing works
Pelka, O., Sigle, S., Werner, P., Schweizer, S. T., Iancu, A., Scherer, L., Kamzol, N. A., Eil, J. H., Apfelbacher, T., Seletkov, D., et al. (2025) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-2651-6653 · Cited by 1 · see citing works
Hina, A., Abbasi, A., Chaudhry, A., Sanaullah, T., Arshad, M., Sarwar, H. M., Sarfaraz, S., Karikari, B., & Kavhiza, N. J. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1865665 · Cited by 1 · see citing works
Cascade Temporal Convolutional Network for Multitask Learning
Zhang, H., Huang, H., & Han, H. (2023) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd57115.2023.10206379 · Cited by 1 · see citing works
Self-citationKumar, A. K., Sarveswaran, S., Dheerthi, N., & Murugarajan, A. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-5767-5.ch015 · Cited by 1 · see citing works
Using amino acid features to identify the pathogenicity of influenza B virus
Kou, Z., Fan, X., Li, J., Shao, Z., & Qiang, X. (2022) · Infectious Diseases of Poverty · doi:10.1186/s40249-022-00974-0 · Cited by 1 · see citing works
Sriram, V., Conard, A. M., Rosenberg, I., Kim, D., & Hall, A. K. (2024) · medRxiv · doi:10.1101/2024.03.15.24304358 · Cited by 1 · see citing works
scMoE: single-cell Multi-Modal Multi-Task Learning via Sparse Mixture-of-ExpertsPreprint
Yun, S., Peng, J., Lee, N., Zhang, Y., Park, C., Liu, Z., & Chen, T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.12.623336 · Cited by 1 · see citing works
Explainable t-SNE for Single-Cell RNA-seq Data Analysis: A Cell-Driven Information Fusion FrameworkPreprint
Han, H., Zhang, T., Niu, L., Wang, J., Han, F., Li, C., Li, J., & Huaixin, C. A. O. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7541980 · Cited by 1 · see citing works
Self-citationArtificial Intelligent-Powered Detection of TuberculosisPreprint
Ibrahim, A. U., Babiker, M. S., Hartati, Y. W., & Hartati, Y. W. (2022) · Preprints.org · doi:10.20944/preprints202209.0208.v1
Using a modified U-net model to improve Retinal Vessel Segmentation
Ziyin, W., Hanwei, Z., Jiachun, H., Ziya, G., & Xiyuan, G. (2023) · Cambridge Explorations in Arts and Sciences · doi:10.61603/ceas.v1i1.8
Breathing new life into asthma research: a review of machine learning and multi-omics approaches
Fontanella, S., Cucco, A., & Čustović, A. (2023) · Minerva Respiratory Medicine · doi:10.23736/s2784-8477.23.02068-5
STRATEGIES, TECHNIQUES, APPLICATIONS AND RESOURCES
Volpato, A. N., Balaji, B., Karthikeyan, D. S., & Panjwani, D. R. (2023) · Jupiter Publications Consortium eBooks · doi:10.47715/jpc.b.978-93-91303-83-9
Towards Explainability in Automated Medical Code Prediction from Clinical Records
Kanimozhi, U., Francis, S., Sun, W., & Moens, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-47718-8_40
Additional file 1 of Using amino acid features to identify the pathogenicity of influenza B virus
Kou, Z., Fan, X., Li, J., Shao, Z., & Qiang, X. (2022) · Figshare · doi:10.6084/m9.figshare.19711327
Emerging applications of artificial intelligence in analyzing EEG signals for the healthcare sector
Irfan, N., Dave, S., & Veer, V. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-21870-5.00020-0
Optimally Traversing Explainability in Bayesian Networks via the Graphical Lasso
Derks, I. P., de Waal, A., Smith, J., Loots, M. T., & Stander, J.-P. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-78255-8_2
How Much Do Activity Patterns Change With Age?
Jurčić, K., Gómez, A. J. R., Zequera, M. L., & Magjarević, R. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-86323-3_19
Enhancing Reliability in COVID-19 Classification from CT scans using Learning to Reject
Duong, B., Nguyễn, H. T., Phan, N., & Do, C. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254549
From Compliance to Consciousness
Rismawati, R., Nur, M., Santaria, R., Supriadi, S., & Amir, A. S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-3912-2.ch011
An Explainable Artificial Immune System (XAIS) for Classification in Biomedical Classification Tasks
Delena-García, P. I., Villuendas-Rey, Y., Mora-Guerrero, L. S., & Alarcón-Paredes, A. (2026) · International Journal of Combinatorial Optimization Problems and Informatics. · doi:10.61467/2007.1558.2026.v17i2.1278
Knowledge Organization in AI-Era Biological Sciences: A Thematic Review
Liu, Q. Y., & Qin, J. (2026) · KNOWLEDGE ORGANIZATION · doi:10.31083/ko46546 · Cited by 1 · see citing works
AI-Powered Risk Assessment and Failure Prediction in Smart Structural Systems
Kaur, S., Akhai, S., Wani, S. A., Abbass, M., Wadhwa, A. S., & Chauhan, A. (2026) · Atlantis Highlights in Materials Science and Technology/Atlantis highlights in materials science and technology · doi:10.2991/978-94-6239-695-1_9
Filippi, M., & Katzschmann, R. K. (2026) · Bio-Design and Manufacturing · doi:10.1631/bdm.2500560
Clinically Aligned Explainable Machine Learning Models for Classifying Migraine Types
Zamil, M. F., & Hameed, D. H. (2026) · Iraqi Journal for Computers and Informatics · doi:10.25195/ijci.v52i2.761
A multi-method analysis of social determinants and non-communicable diseases
Ma, Y. (2026) · AIP conference proceedings · doi:10.1063/12.0047155
- Authors
- Han, H., & Liu, X.
- Affiliations
- Baylor University (US)
- Xiamen University (CN)
- Corresponding author
- Han, Henry
- Venue
- BMC Bioinformatics
- Published
- 2021-12
- Volume
- 22
- Issue
- S12
- Pages
- 443-443
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC8772040
- OpenAlex
- W4205631037
- Cited by
- 59
- References
- 3
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 17 · 2023: 15 · 2022: 11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Baylor University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- explainable AI, interpretability, biomedical data science, deep learning interpretability, model robustness, trustworthiness, transparency, AI decision-making, disease diagnosis, bioinformatics
- MeSH terms
- Data Science, Artificial Intelligence, Delivery of Health Care
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
