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.
“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]
The Introduction (page unavailable) says AI has advanced digital pathology in education, clinical diagnosis, and image analysis; the AI and Education and AI and Image Analysis sections describe applications of deep-learning methods. The Integration of AI with Other Clinical Data section discusses linking pathology images with clinical information, but presents much of that integration as a goal or expected development rather than an application already expanded by deep learning. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThese datasets have enabled utilization of AI techniques in various downstream tasks, such as sample classification, prognosis prediction, image lesion detection, image segmentation, and others, which have demonstrated higher precision and efficacy in clinical settings.
“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]
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Experimental — accuracy not yet measuredThe digitalization of oncology, including electronic medical records, radiology and pathology imaging, and routine genomic profiling, is advancing but is uneven across data types and clinical sites.
“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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- 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 · 2012: 1
- 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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