The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Digital pathology and artificial intelligence
Open accessAbstract
In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.
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“The development of deep learning methods has expanded the applications of digital pathology in education, clinical diagnosis, image analysis, and integration with other clinical diagnostic methods [94].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [94]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“These datasets have enabled the utilization of AI techniques in various downstream tasks, such as sample classification, prognosis prediction, image lesion detection, image segmentation, and others [206], which have demonstrated higher precision and efficacy in clinical settings [94].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [94]
Experimental — accuracy not yet measuredIn the citing paper
“This includes the storage of patient data in electronic medical record (EMR) systems, the digitization of radiology and pathology imaging (3), and the increasing adoption of routine genomic profiling (4).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [3]
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Research works printed but not linked: 1
- Niazi, 2018, Automated T1 bladder risk stratification based on depth of lamina propria invasion from H&E tissue biopsies: a deep learning approach, SPIE Medical Imaging, 10581, 105810H1
Cited by
Sorted by citations
Data-efficient and weakly supervised computational pathology on whole-slide images
Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00682-w · Cited by 2,282
Crosby, D., Bhatia, S., Brindle, K. M., Coussens, L. M., Dive, C., Emberton, M., Esener, S., Fitzgerald, R. C., Gambhir, S. S., Kuhn, P., et al. (2022) · Science · doi:10.1126/science.aay9040 · Cited by 1,379
Breast cancer: pathogenesis and treatments
Xiong, X., Zheng, L., Ding, Y., Chen, Y., Cai, Y., Wang, L., Huang, L., Liu, C., Shao, Z., & Yu, K. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-02108-4 · Cited by 1,170
Deep neural network models for computational histopathology: A survey
Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749
Deep Visual Proteomics defines single-cell identity and heterogeneity
Mund, A., Coscia, F., Kriston, A., Hollandi, R., Kovács, F., Brunner, A.-D., Migh, E., Schweizer, L., Santos, A., Bzorek, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01302-5 · Cited by 562
Metadata
- Authors
- Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N.
- Affiliations
- Atrium Health Wake Forest Baptist (US)
- The Ohio State University (US)
- Corresponding author
- Niazi, Muhammad Khalid Khan
- Venue
- The Lancet Oncology
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 20
- Issue
- 5
- Pages
- e253-e261
- ISSN
- 1470-2045 (ISSN-L)
- PMC
- PMC8711251
- OpenAlex
- W2943370629
- Cited by
- 1,232
- References
- 61
- Citations by year
- 2026: 164 · 2025: 204 · 2024: 236 · 2023: 209 · 2022: 175 · 2021: 138 · 2020: 91 · 2019: 12
- FWCI
- 6.83
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- The Lancet Oncology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute and The Ohio State University Comprehensive Cancer Center Intramural Research (R01CA134451)
- Metin N Gurcan (co-principal investigator) and Gerard Lozanski (U24CA199374)
- 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
- digital pathology, artificial intelligence, whole slide imaging, computer-aided diagnosis, cancer diagnosis
- MeSH terms
- Artificial Intelligence, Diagnosis, Computer-Assisted, Humans, Image Interpretation, Computer-Assisted, Microscopy, Neoplasms, Pathology, Predictive Value of Tests, Prognosis, Reproducibility of Results, Workflow
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