Cited by 1,232AI in cancer detectionArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

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Digital pathology and artificial intelligence

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Niazi, Muhammad Khalid Khan, Parwani, Anil V, Gurcan, Metin N·2019·The Lancet Oncology

DOI 10.1016/s1470-2045(19)30154-8PMC PMC8711251

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Abstract

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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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
ORCID: Niazi, Muhammad Khalid Khan
Parwani, Anil V
Venue: The Lancet Oncology
Publisher: Elsevier BV
Published: 2019-05
Volume: 20
Issue: 5
Pages: e253-e261
ISSN: 1470-2045 (ISSN-L)
DOI: 10.1016/s1470-2045(19)30154-8
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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