The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Artificial intelligence and computational pathology
Open accessAbstract
Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, into clinical practice unlocks innovative approaches for patient care. Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics. However, computational pathology faces several challenges, including the ability to integrate raw data from different sources, limitation of hardware processing capacity, and a lack of specific training programs, as well as issues on ethics and larger societal acceptable practices that are still solidifying. The establishment of the entire industry of computational pathology requires far-reaching changes of the three essential elements connecting patients and doctors: the local laboratory, the scan center, and the central cloud hub/portal for data processing and retrieval. Computational pathology, unlocked through information integration and advanced digital communication networks, has the potential to improve clinical workflow efficiency, diagnostic quality, and ultimately create personalized diagnosis and treatment plans for patients. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Computational pathology aims to reduce diagnostic and classification errors while accelerating biomarker discovery, pathophysiology evaluations, animal research, and toxicological assessments (40).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [40]
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.
Citations
Cites
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698
Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232
Image analysis and machine learning in digital pathology: challenges and opportunities
Madabhushi, A., & Lee, G. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.06.037 · Cited by 1,093
Predicting cancer outcomes from histology and genomics using convolutional networks
Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084
Deep Learning for Identifying Metastatic Breast CancerPreprint
Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A. H. (2016) · arXiv.org · doi:10.48550/arxiv.1606.05718 · Cited by 1,028
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.086 · Cited by 1,027
Machine Learning Methods for Histopathological Image Analysis
Komura, D., & Ishikawa, S. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.001 · Cited by 937
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification
Hou, L., Samaras, D., Kurç, T., Gao, Y., Davis, J., & Saltz, J. H. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.266 · Cited by 921
Cited by
Sorted by citations
Items 501–542 of 542
郑, 瑶. (2026) · Journal of Clinical Personalized Medicine · doi:10.12677/jcpm.2026.52112
Flannery, B. T., DeSilvio, T., & Viswanath, S. E. (2026) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.3085615
Attention Alignment for Single Domain Generalization in Histopathology
He, Y., 宣浩, Zemp, R. J., & Li, X. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462561
SLDPC: Slide-Level Dual-Prompt Collaboration for few-shot whole slide image classification
Yuan, L., Zheng, Y., Liu, W., Zhao, H., Zhang, W., Wei, B., & Chen, L. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102768
Ullah, E., Mishra, K., & Parwani, A. V. (2026) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000536
Pixelomics: The Omics-Style Interrogation of Whole Slide Images for Precision Pathology
Ullah, E., & Parwani, A. V. (2026) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000535
Blok Zinciri Teknolojisinin Sağlıkta Uygulanabilirliğine Yönelik Bir Ölçek Geliştirme Çalışması
Türkoğlu, Ç., & Çelik, R. (2026) · Bilecik Şeyh Edebali Üniversitesi Sağlık Bilimleri Fakültesi Dergisi · doi:10.61535/bseusbfd.1685773
Perspective Chapter: Machine Learning Models in Early Diagnosis and Risk Prediction of Skin Cancers
Ivanovic, N., Mercioni, M. A., & Dumache, O. T. A. R. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1013670 · Cited by 1
Griffith, J. L., Cruz‐Almeida, Y., Almarza, A. A., Caudle, R., & Allen, K. D. (2026) · JBMR Plus · doi:10.1093/jbmrpl/ziag039
Neshat, N., Jain, K., & Gupta, S. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01169-4
Zheng, Q., Mei, H., Weng, X., Yang, R., Wang, K., Ni, X., Wu, J., Fan, J., Liu, T., Yuan, J., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01443-9
Sharma, A., Nateghi, R., Ayad, M. A., Cooper, L., & Goldstein, J. A. (2026) · Placenta · doi:10.1016/j.placenta.2026.05.028
Extrachromosomal DNA as a Causal Instrument for Spatial Multi-OmicsPreprint
Craig, D. W., & Rodin, A. S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.02.729483
Wang, H., Cong, B., Cho, W. C., Qi, G., Li, Y., Zhang, L., & Zhu, Z. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115313
Borrecho, G., Nina, P., Santos, R., Ferreira, I., Salgueiro, P., Madeira, C. G., Rato, L. M., & Oliveira, R. C. (2026) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2026.100682
Liang, M., Wang, X., Li, B., Zhang, Y., Zhang, Y., Jia, X., & Wang, L. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3702808
Clinical artificial intelligence applications of vision-language foundation models
Thirunavukarasu, A. J., Li, S. Y., Qin, P., Nie, D., Sanghera, R., Lim, E., Yu, J., & Zhang, L. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001453
Ling, Z., Tang, Z., Wu, J., Rui, D., & Zhang, Z. (2026) · Genes & Diseases · doi:10.1016/j.gendis.2026.102304
Shah, N. A., Sarwar, M., & Ullah, E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.25.734452
An enhanced spatial-frequency fusion modulation for multi-instance learning on whole slide images
Deng, B., Yin, Y., Qin, C., Mai, Z., Yan, W. J., Ren, M., Zhou, Y., Zhou, Z., Xie, Z., Xiao, Y., et al. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116582
Yin, X.-L., Wang, B.-Y., & Song, G.-L. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114446
Zhang, X., Xu, J., Li, J., Liu, M., Jiao, Y., & Cai, C. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3510-0_14
Pascarella, C. B., Giobbe, M., Maffei, E., Ruotolo, R., Ciaparrone, C., Ciancia, G., D’Antonio, A., Verze, P., Zeppa, P., & Caputo, A. (2026) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-026-04645-5
Haisch, N., Liesche‐Starnecker, F., Grambow, G., & Hieber, D. (2026) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2026.100701
Navigating WSI with RL: Potentials and Challenges
Mohamad, M., Ponzio, F., Gassier, M., Poté, N., Ambrosetti, D., & Descombes, X. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-34456-4_7
Alawode, B., Abdalla, M. A., Mahapatra, D., Naseer, M., & Javed, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36969-7_9
Nuclear Morphology Tracks Molecular Class Unevenly Across Four CancersPreprint
Şimşek, G., & Kaymaz, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-10623310/v1
Yan, S., Gao, T.-T., & Zhao, X. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1846143
Chen, H., Tong, L., Liu, J., Liu, K., Li, X., Shi, S., Xi, S., Tian, G., Zhang, M., Zhong, D., et al. (2026) · Frontiers in Big Data · doi:10.3389/fdata.2026.1924721
Pixelomics: The pixelome as a foundation of precision pathology
Ullah, E., Hu, J., Rashidi, H., Pantanowitz, L., Li, Z., & Parwani, A. V. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40383-5.00016-9
Queue-Aware Digital Pathology Triage: A Prioritization Framework for High-Risk Specimen Review
Ivanov, Y., & Petrov, P. (2026) · Journal of Health Informatics and Digital Systems · doi:10.68159/c257477447
Chen, J., Li, J., Liu, M., Chen, D., Li, J., Song, Z., & Cai, C. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.116382
PyraE2E: Enhancing End-to-End WSI Analysis via Cross-Scale Super-Resolution
Lin, Y., Liu, Y., Zhang, W., Fan, Y., Wang, Z., Shen, D., Fei, L., Liu, X., & Xu, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37261-1_10
Biotechnology-Enabled Artificial Intelligence for Veterinary Histopathology and Disease Detection
Essa, I. M., Lokeshwari, B., Saranraj, P., Kesavardhini, K., Kandhan, K., & Kanshiya, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1358-8.ch008
Artificial Intelligence in Veterinary Histopathology
Kandhan, K., Subramanian, R., Ali, A. M., Gayathri, K. V., Manigandan, M., & Sivasakthivelan, P. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1358-8.ch001
Wang, Z.-Y., Liu, N., & Chen, M. (2026) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2026.1875788
A multi-model deep learning approach to detect tumors on Mohs micrographic surgery slides
Yılmaz, A., Aydin, S. A., Temur, D., Yuceyalcin, F., Kahya, B. D., Varol, R., GÖKÖZ, Ö., Gençoğlan, G., ÜVET, H., & Elçin, G. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05559-0
Xie, Y., Yin, W., Liu, Z., Qi, H., Zhao, J., Hu, X., & Zhang, Z. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109786
Özer, E., Alchami, F., Gilal, N. U., & Boughorbel, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45156-0.00006-6
Özbek, B., Dogan, H. N., Vural, Ç., Yetim, K., Çelik, A., Urhan, O., & Bayrak, B. Y. (2026) · International Journal of Surgical Pathology · doi:10.1177/10668969261489627
Zhu, M., Aschenaki, Y., Zhang, X., Sali, R., Lin, Z., Dixon, C. M., & Zhang, D. Y. (2026) · BMJ Oncology · doi:10.1136/bmjonc-2026-001208
Self-citationInterpreting interpretable AI in digital pathology
Wang, Z., Popel, A. S., & Sulam, J. (2026) · Patterns · doi:10.1016/j.patter.2026.101664
Metadata
- Authors
- Cui, M., & Zhang, D. Y.
- Affiliations
- St. Luke's-Roosevelt Hospital Center (US)
- Veterans Health Administration (US)
- Corresponding author
- Zhang, David Y.
- Venue
- Laboratory Investigation
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 101
- Issue
- 4
- Pages
- 412-422
- ISSN
- 0023-6837 (ISSN-L)
- PMC
- PMC7811340
- OpenAlex
- W3121943121
- Cited by
- 544
- References
- 64
- Citations by year
- 2026: 68 · 2025: 103 · 2024: 126 · 2023: 127 · 2022: 97 · 2021: 23
- FWCI
- 6.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Laboratory Investigation (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- computational pathology, artificial intelligence in pathology, whole slide images, multi-omics integration, clinical informatics, cloud processing, clinical workflow efficiency, personalized diagnosis, AI ethics
- MeSH terms
- Artificial Intelligence, Humans, Medical Informatics, Pathology, Clinical, Statistics as Topic, Computational Biology
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
