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.
“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]
The Introduction says that reducing diagnostic and classification errors is a major advantage of computational pathology. The Pathologist-centered medical system section describes automated biomarker assessment, but not accelerated biomarker discovery; the passages do not establish the claimed acceleration of pathophysiology evaluations, animal research, or toxicological assessments. No page numbers are available in our copy. (LLM, conf=0.86)
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- 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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