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.
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“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]
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
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Opportunities and obstacles for deep learning in biology and medicine
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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
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Image analysis and machine learning in digital pathology: challenges and opportunities
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Predicting cancer outcomes from histology and genomics using convolutional networks
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Deep Learning for Identifying Metastatic Breast CancerPreprint
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Machine Learning Methods for Histopathological Image Analysis
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Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification
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Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm
Krishnan, G., Singh, S., Pathania, M., Gosavi, S., Abhishek, S., Parchani, A., & Dhar, M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1227091 · Cited by 337
On the use of AI-based tools like ChatGPT to support management research
Burger, B., Kanbach, D. K., Kraus, S., Breier, M., & Corvello, V. (2023) · European Journal of Innovation Management · doi:10.1108/ejim-02-2023-0156 · Cited by 310
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Future of Artificial Intelligence—Machine Learning Trends in Pathology and Medicine
Hanna, M. G., Pantanowitz, L., Dash, R., Harrison, J. H., Deebajah, M., Pantanowitz, J., & Rashidi, H. H. (2025) · Modern Pathology · doi:10.1016/j.modpat.2025.100705 · Cited by 231
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
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