Cited by 544AI in cancer detectionArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

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Artificial intelligence and computational pathology

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Cui, Miao, Zhang, David Y.·2021·Laboratory Investigation

DOI 10.1038/s41374-020-00514-0PMC PMC7811340

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Abstract

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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Authors: Cui, M., & Zhang, D. Y.
Affiliations: St. Luke's-Roosevelt Hospital Center (US)
Veterans Health Administration (US)
Corresponding author: Zhang, David Y.
ORCID: Cui, Miao
Zhang, David Y.
Venue: Laboratory Investigation
Publisher: Elsevier BV
Published: 2021-04
Volume: 101
Issue: 4
Pages: 412-422
ISSN: 0023-6837 (ISSN-L)
DOI: 10.1038/s41374-020-00514-0
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 650 d)
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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