The DOI matches its Crossref record; 56 of the 58 references it cites were found in a registry or an index.
Artificial neural networks in medical diagnosis
Open accessAbstract
An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can be employed. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected examples.
Overview
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
Verification
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?
In the citing paper
“The ability of the NN is to model highly nonlinear systems such as physiological records where the correlation of the input parameters is not easily detectable [71].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [71]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Artificial neural networks: fundamentals, computing, design, and application
Basheer, I. A., & Hajmeer, M. N. (2000) · Journal of Microbiological Methods · doi:10.1016/s0167-7012(00)00201-3 · Cited by 3,316
Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y.-C., Baker, J. A., & Tourassi, G. D. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.12.031 · Cited by 797
Neural networks in chemistry and drug design
Zupan, J., & Gasteiger, J. (1999) · Medical Entomology and Zoology · Cited by 718
An introduction to neural computing
(1990) · Biological Psychology · doi:10.1016/0301-0511(90)90019-s · Cited by 621
Kubát, M. (2017) · Springer eBooks · doi:10.1007/978-3-319-63913-0_5 · Cited by 379
Artificial Intelligence in Medical Diagnosis
Szolovits, P., Patil, R. S., & Schwartz, W. B. (1988) · Annals of Internal Medicine · doi:10.7326/0003-4819-108-1-80 · Cited by 285
Feature selection with neural networks
Verikas, A., & Bačauskienė, M. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(02)00081-8 · Cited by 253
Tate, A. R., Underwood, J., Acosta, D., Julià‐Sapé, M., Majós, C., Moreno‐Torres, À., Howe, F. A., van der Graaf, M., Lefournier, V., Murphy, M. M., et al. (2006) · NMR in Biomedicine · doi:10.1002/nbm.1016 · Cited by 227
Artificial neural networks for diagnosis and survival prediction in colon cancer
Ahmed, F. (2005) · Molecular Cancer · doi:10.1186/1476-4598-4-29 · Cited by 223
Segmentation of multiple sclerosis lesions in MR images: a review
Mortazavi, D., Kouzani, A. Z., & Hamid Soltanian-zadeh, A. (2011) · Neuroradiology · doi:10.1007/s00234-011-0886-7 · Cited by 146
Research works printed but not linked: 5
- 10.1371/journal.pone.0044587.t004 · DOI
- 10.1002/1097-0142(20010415)91:8+%3C1615::AID-CNCR1175%3E3.0.CO;2-L · DOI
- Gannous AS, 2011, Improving an Artificial Neural Network Model to Predict Thyroid Bending Protein Diagnosis Using Preprocessing Techniques, WASET, 50, 124
- 10.1371/journal.pone.0029179.t004 · DOI
- 10.1007/s00234-011-0886-7 · DOI
Cited by
Sorted by citations
Items 801–900 of 905
Artificial Neural Networks in Medico-Diagnostics
Zacharia, N., Ghosh, M., & Howlader, M. M. R. (2021)
Predicting diarrhoea outbreak with climate change
Abdullahi, T. (2021) · Open University of Cape Town (University of Cape Town)
Umasankar, P., & Thiagarasu, V. (2019) · Asian Journal of Engineering and Applied Technology · doi:10.51983/ajeat-2019.8.1.1068
Opto-Electronic Neural Networks Based on Few-Mode Fiber
Xu, J., Zhao, J., Xu, T., & Wong, K. K. Y. (2021) · International Conference on Optical Communications and Networks (ICOCN) · doi:10.1109/icocn53177.2021.9563734
Machine Learning Enhanced Decision-Making
Rybak, N., & Hassall, M. E. (2021) · Springer eBooks · doi:10.1007/978-3-030-58675-1_20-1
Study on the ANN Forecasting of Epidemical Diseases
Sahoo, N. K. (2022) · Materials horizons · doi:10.1007/978-981-16-4372-9_8
Perlich, A. C. (2022) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-9208 · Cited by 2
Cavallín, A., Frutos, M., Vigier, H. P., & Rossit, D. G. (2021) · IGI Global eBooks · doi:10.4018/978-1-6684-2408-7.ch026
An Expert Selection System for Medical Diagnosis
(2016) · International Journal of Science and Research (IJSR) · doi:10.21275/v5i6.nov164615
(2018) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-5225-5580-3.ch001
(2019) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83867-171-620191008
A Predictive E-Health Information System
Al-Khasawneh, A., & Hijazi, H. (2016) · IGI Global eBooks · doi:10.4018/978-1-5225-0159-6.ch018
Decision Support System for Diabetes Classification Using Data Mining Techniques
Al-Khasawneh, A. M. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-9023-2.ch053
A Method for Classification Using Data Mining Technique for Diabetes
Al-Khasawneh, A. (2016) · IGI Global eBooks · doi:10.4018/978-1-5225-0788-8.ch017
de Oliveira, G. O. (2020) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.3.2020.tde-01042021-085104
A Method for Classification Using Data Mining Technique for Diabetes
Al-Khasawneh, A. (2016) · IGI Global eBooks · doi:10.4018/978-1-5225-0159-6.ch030
Estimating daily global evapotranspiration
Raoufi, R. (2019) · doi:10.17760/d20323944
A Method for Classification Using Data Mining Technique for Diabetes
Al-Khasawneh, A. (2019) · IGI Global eBooks · doi:10.4018/978-1-5225-9863-3.ch006
Determining Headache Diseases With Genetic Algorithm
Çelik, G. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-8048-6.ch033
An Artificial Intelligence Approach to Thrombophilia Risk
Vilhena, J., Vicente, H., Martins, M. R., Grañeda, J. M., Caldeira, F., Gusmão, R., Neves, J., & Neves, J. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-7122-3.ch009
Brain Disease Detection Using Machine Learning
Bashir, M. K. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.44466
Using ARIMA and BP neural network to analyse incidence rate of AIDS in China
Yang, Q., Gao, X., Zhang, J., & Ma, H. (2022) · SHS Web of Conferences · doi:10.1051/shsconf/202214001048
Improving Adversarial Robustness of Deep Neural Networks via Linear Programming
Tang, X., Yang, Z., Fu, X., Wang, J., & Zeng, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10363-6_22
Pérez, E. M., & Martín, Q. M. (2022) · Biomedical Statistics and Informatics · doi:10.11648/j.bsi.20220701.12
Computational Intelligence Paradigms in Radiological Image Processing—Recent Trends and Challenges
Gavade, A. B., Nerli, R. B., Patil, A. S., Ghagane, S. C., & Bhagavatula, V. S. P. (2022) · Wiley eBooks · doi:10.1002/9781119813439.ch6
Framework for Brain Disease Detection Using Machine Learning Approach
Bashir, M. K. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.44436
Târcoveanu, F. (2022) · Jurnalul de Chirurgie · doi:10.7438/jsurg.2022.03.02
Karwasra, R., Khanna, K., Sharma, N., Malhotra, R., Sharma, S., Singh, S., Ali, A., & Verma, S. M. (2022) · Studies in computational intelligence · doi:10.1007/978-981-19-6379-7_11
Implementation of a neural network of low computational cost for its application in arm prostheses
LEÓN-PERALTA, J. L., Sánchez‐Lara, R., Vazquez-Avila, J. L., & Vargas, J. I. Y. (2022) · Revista de Ingeniería Tecnológica · doi:10.35429/jten.2022.17.6.27.34
Prediction of Corona-Virus Using Deep Learning
Al-Ali, L. (2022) · Tikrit Journal of Pure Science · doi:10.25130/tjps.v27i1.89
Adaptive and Biocompatible Technologies
Zniber, M., Khatib, M., & Huynh, T. (2022) · Royal Society of Chemistry eBooks · doi:10.1039/9781839166990-00416
Predicting liver disease using automatic classification model
Gajendran, G., & Priyanka, R. (2022) · AIP conference proceedings · doi:10.1063/5.0110047
Fuzzy Expert System for Rectal Cancer Based on Possibility Measure
Gardashova, L. A., Aliyarov, Y. R., & Ahmadov, S. A. (2023) · Artificial intelligence · doi:10.5772/intechopen.109405
Bio-inspired Flower Pollination Algorithm for the Optimization of a Monolithic Neural Network
Carreon-Ortiz, H., Melín, P., & Valdez, F. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-22042-5_9
A Comprehensive Performance Analysis on Artificial Neural Networks
Panigrahi, S., Subudhi, S., & Ninoria, S. Z. (2022) · International Conference on System Modeling & Advancement in Research Trends (SMART) · doi:10.1109/smart55829.2022.10047509
Medical Data Analytics: Techniques, Challenges, and Opportunities
Agnihotri, A. (2020) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v11i1.13569
Mehrad, M., Nojavan, M., Raissi, S., & Javadi, M. (2022) · مجله دانشگاه علوم پزشکی اراک · doi:10.32598/jams.25.2.6450.1
Brückerhoff‐Plückelmann, F., FELDMANN, J., & Pernice, W. H. P. (2023) · Wiley eBooks · doi:10.1002/9781394228713.ch9
Abotaleb, A. M., Mohammed, A. A., Saber, M. T., & Riad, M. (2023) · International Telecommunications Conference (ITC-Egypt) · doi:10.1109/itc-egypt58155.2023.10206160
kProp: Multi-neuron Relaxation Method for Neural Network Robustness Verification
Xue, X., Zhang, X., & Sun, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42441-0_11 · Cited by 1
Image Segmentation and Pre-Processing for Lung Cancer Detection in Humans Based on Deep-Learning
Singh, D., & Singh, J. (2023) · International Conference on Circuit, Power and Computing Technologies · doi:10.1109/iccpct58313.2023.10244814
Analysis on Computer-Aided Diagnosis of Breast Tumors Based on GA-SVM
Zhou, S., & Liu, X. (2023) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc58761.2023.00017
The Effect of Varying Training on Neural Network Weights and Visualizations
Fountain, E., & Rasmus, J. (2019) · Journal of Emerging Investigators · doi:10.59720/19-067
An Edge-Based Multi-Technology Architecture Healthcare Industry 4.0
Jesme, C. M. A., Adlin, M. T., & Kenz, A. (2023) · International Journal of Scientific Research in Science and Technology · doi:10.32628/ijsrst52310548
A Multifactorial Approach to Explain Risk Features for Predicting Survival Rate of Heart Failure
Xue, L., & Lu, W. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-47126-1_11
Roy, S. D., & Bhowmik, M. K. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3346496
Artemuk, S., & Mykytyn, I. (2023) · Key title Zbìrnik naukovih pracʹ Odesʹkoï deržavnoï akademìï tehnìčnogo regulûvannâ ta âkostì · doi:10.32684/2412-5288-2023-2-23-41-51
Smart Process Failure Analysis based on Bayesian Networks and Knowledge Graphs
Müller, A., Simon, L., Petrović, O., Herfs, W., & Brecher, C. (2024) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic60209.2024.10463268
Park, M.-H., Hur, J.-J., Yun, G.-H., & Lee, W.-J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4806941
Karwasra, R., Khanna, K., Sharma, N., Malhotra, R., Sharma, S., Singh, S., Ali, A., & Verma, S. M. (2024) · Springer eBooks · doi:10.1007/978-981-99-7808-3_11
Advances in Breast Cancer Detection Techniques: A Comprehensive Survey
Al-Janabi, M. S., & Al-Sultan, A. Y. (2024) · Journal of University of Babylon for Pure and Applied Sciences · doi:10.29196/4q5rj473
A neural network simulator for educational purposes
Kesen, U., & Alsan, S. (2024) · Journal of Mechatronics and Artificial Intelligence in Engineering · doi:10.21595/jmai.2024.24085
Application of Artifical Neural Networks for Processing Some Biomedical Data
Štork, M., & Novak, J. (2024) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco62516.2024.10577768
Sumiati, S., Fernando, D., Hasoloan, H. I., & Purnamasari, M. (2024) · ILKOM Jurnal Ilmiah · doi:10.33096/ilkom.v16i1.1927.100-107
An Intensive Review Analysis about Fuzzy Logic usage in Smart Medical Care System
Kumar, V., Saini, D., Rastogi, S., Raman, R., Verma, A., & Meenakshi, R. (2024) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite60783.2024.10616877
Handika, A. D., Putri, I. P., Handoko, A. W., Hunou, S., & Sugiyantari, S. (2024) · AIP conference proceedings · doi:10.1063/5.0228005
USING EVOLUTIONARY ALGORITHMS FOR TRAINING AND OPTIMIZING NEURAL NETWORKS
Hirianskyi, B., & Bulakh, B. (2024) · Наука і техніка сьогодні · doi:10.52058/2786-6025-2024-8(36)-808-823
Climate Resilience Enhancement, Air Pollution Detects from Images using CNN
Lakshmi, C., Muppidi, S., Maram, B., Creesy, R., Merugula, S., & Rachapudi, V. (2024) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici62503.2024.10696471
Predictive analysis of urban wastewater capacity using ANN and ODE model: A case study
Arslan, R. S., Taşyürek, M., Daşbaşı, B., & Daşbaşı, T. (2024) · Environmental Research and Technology · doi:10.35208/ert.1515094
Taha, A. H., Ajel, A. R., Kadhim, M. A., Abed, J. K., & Ali, Z. B. (2024) · AIP conference proceedings · doi:10.1063/5.0236261
Using Artificial Neural Networks to Solve the Gross–Pitaevskii Equation
Tsoulos, I. G., Stavrou, V. N., & Tsalikakis, D. (2024) · Axioms · doi:10.3390/axioms13100711
Healthcare Data Analytics Based on Machine Learning
Gаnchеvа, V. (2024) · International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) · doi:10.1109/ccci61916.2024.10736473
Kumar, K., Sharma, M. K., & Mukherjee, R. (2024) · Advances in intelligent systems and computing · doi:10.1007/978-981-97-6352-8_38
Creating Partnership With The Aid Of AI Technology In Health SectorPreprint
Owhorji, P. E. B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5023058
Kaur, P., Kaur, R., Singh, R., Singh, S., & Kaur, G. (2024) · Journal of Optics · doi:10.1007/s12596-024-02426-4
Kolosov, G. A., Shorokhov, A. S., & Fedyanin, A. A. (2024) · Письма в Журнал экспериментальной и теоретической физики · doi:10.31857/s0370274x24120227
Postoperative Apnea‐Hypopnea Index Prediction of Velopharyngeal Surgery Based on Machine Learning
You, J., Li, J., Zhou, Y., Cao, X., Zhao, C., Zhang, Y., & Ye, J. (2025) · OTO Open · doi:10.1002/oto2.70061
Smart Healthcare Systems and Predictive Medicine Using Machine Learning
Guntupalli, A., Muddana, A. K., Turlapati, P. K., Paturi, S., & Chalapathi, M. M. V. (2025) · Cognitive science and technology · doi:10.1007/978-981-97-8533-9_25
Opportunities and challenges: Using artificial intelligence in the healthcare sector
Thakre, G., Saratkar, S., Raut, R., Chaudhari, A., & Thute, T. (2025) · AIP conference proceedings · doi:10.1063/5.0243580
The Use of Artificial Intelligence in Disease Diagnosis: A Systematic Literature Review
Lutviana, L., & Mangkunegara, I. S. (2024) · Journal of Advanced Health Informatics Research · doi:10.59247/jahir.v1i3.172
Ramesh, D., Rizwana, & Baira, M. S. (2025) · International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) · doi:10.1109/isacc65211.2025.10969371
Uddin, M. A., Roy, S., Tamanna, T., & Rimon, R. A. (2025) · Journal of Advanced Transportation · doi:10.1155/atr/5625483
Meng, X., Liu, L., Wu, S., Chen, R., & Bai, Y. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-92545-0_4
Disentangled Representation Learning and its Application in Clinical Diagnosis
Cui, Y. (2025) · Applied and Computational Engineering · doi:10.54254/2755-2721/2025.po24519
Comprehensive View from Ethics to AI Ethics
Budhiraja, K., Kaur, G., Rani, Y., & Jha, R. (2025) · Wiley eBooks · doi:10.1002/9781394275311.ch10
Introduction to AI and Computational Intelligence in Healthcare
Sivakarthi, G., Vinora, A., Deborah, R. N., & Soundarya, M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3311-3.ch002
Mohapatra, S., Sarangi, P. P., & Mishra, B. S. P. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-8070-2_33
Anomaly Detection Reliability in Consumer Electronics Manufacturing
Pai, C.-W., Hsueh, H.-W., & Hsu, S.-H. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3626738
Hou, Y., Yu, H., Zhang, Q., Yang, Y., Liu, X., Wang, X., & Jiang, Y. (2023) · Figshare · doi:10.6084/m9.figshare.22606979
Jebreen, K., RADWAN, E. S., Rebai, W. K., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M. (2024) · Figshare · doi:10.6084/m9.figshare.26717358
Jebreen, K., RADWAN, E. S., Rebai, W. K., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M. (2024) · Figshare · doi:10.6084/m9.figshare.26717355
Jebreen, K., RADWAN, E. S., Rebai, W. K., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M. (2024) · Figshare · doi:10.6084/m9.figshare.25770556
Wang, Y., Wang, L., Sun, Y., Wu, M., Ma, Y., Yang, L., Chun, M., Zhong, L., Hossain, M. A., & Peng, B. (2021) · Figshare · doi:10.6084/m9.figshare.14684983
Jebreen, K., RADWAN, E. S., Rebai, W. K., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M. (2024) · Figshare · doi:10.6084/m9.figshare.25770366
22911 Artificial intelligence in diagnostics
Latha, A. H. D. P., Padmavathi, S., Varalakshmi, B., & Padma, D. A. (2025) · De Gruyter eBooks · doi:10.1515/9783111548777-011
Wu, P., Xu, Y., Zhao, Z., Liu, Z., Xiuyan, G. A. O., Ren, L., Zhang, Y., Guo, R., & Yu, H. (2025) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ae2c3b
Fruit Quality Assessment using Supervised Learning Techniques based on Image Features
Gavas, R., Kanikar, D., Kumar, N., & Mishra, G. (2025) · International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU) · doi:10.1109/ic-cgu67042.2025.11338101
Chen, J., Jang, S., & Wang, M. Q. (2026) · Medical Care · doi:10.1097/mlr.0000000000002305
Architectures matérielles numériques intégrées et réseaux de neurones à codage parcimonieux
Wouafo, H. N. (2016) · theses.fr (ABES) · doi:10.70675/a4cc5e39zf913z41ebz9b82zbb678b263f5e
Unravelling the tangle of motor neuron diseases : insights from neuroimaging and neurophysiology
Querin, G. (2019) · theses.fr (ABES) · doi:10.70675/30ad3e2az5051z4364z9ec1z4b8d1f952b7b
Metadata
- Authors
- Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J.
- Affiliations
- Masaryk University (CZ)
- Universidad de Salamanca (ES)
- Venue
- Journal of Applied Biomedicine
- Published
- 2013-07-31
- Volume
- 11
- Issue
- 2
- Pages
- 47-58
- ISSN
- 1214-021X (ISSN-L)
- 1214-0287
- OpenAlex
- W2137687977
- Cited by
- 898
- References
- 58
- Citations by year
- 2026: 7 · 2025: 46 · 2024: 71 · 2023: 69 · 2022: 89 · 2021: 107 · 2020: 104 · 2019: 93 · 2018: 104 · 2017: 70 · 2016: 62 · 2015: 36 · 2014: 32 · 2013: 5
- FWCI
- 77.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Applied Biomedicine (publishedVersion)
- Funders
- Universidad de La Laguna
- Grantová Agentura České Republiky (MSM0021627501, CZ.1.05/1.1.00/02.0123, MSM0021622430, CZ.1.05/2.1.00/03.0086, GA202/07/1669, GA104/08/0229)
- European Regional Development Fund (MSM0021622430, CZ.1.05/1.100, CZ.1.07/2.3.00/20.0185, CZ.1.05/2.1.00/03.0086, CZ.1.05/1.1.00/02.0123.)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial neural networks, computer-aided diagnosis, diagnosis, clinical decision support, adaptive learning algorithms, misdiagnosis prevention
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