Artificial intelligence in digital pathology: Principles, applications, challenges, and future directions.
- 1Trinity School of Medicine, Trinity College Road, Ratho Mill, Kingstown, Saint Vincent and the Grenadines.
Abstract
This article provides an overview of digital pathology and its evolving role in modern diagnostic practice. We begin by comparing conventional microscopy with digital pathology and outlining the major components of the digital pathology workflow. We then explore whole-slide imaging, its imaging modalities, scanner technologies, image storage, and image management systems. We then examine the role of artificial intelligence (AI) in digital pathology, discussing current AI applications and the spectrum of computational approaches, from conventional feature-engineering methods to deep learning and multimodal models. Finally, we review the clinical applications of digital pathology, summarize its implementation across US medical centers, and discuss the technical, regulatory, and operational challenges that must be addressed for broader clinical adoption.
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BibTeX
@article{wang2026artificial,
title = {{Artificial intelligence in digital pathology: Principles, applications, challenges, and future directions.}},
author = {Yifei Wang and Leona Odabachian},
journal = {Journal of pathology informatics},
year = {2026},
month = oct,
doi = {10.1016/j.jpi.2026.100749},
url = {https://europepmc.org/article/MED/42999554},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial intelligence in digital pathology: Principles, applications, challenges, and future directions.
AU - Yifei Wang
AU - Leona Odabachian
PY - 2026
DA - 2026/10/06
JO - Journal of pathology informatics
DO - 10.1016/j.jpi.2026.100749
UR - https://europepmc.org/article/MED/42999554
AB - This article provides an overview of digital pathology and its evolving role in modern diagnostic practice. We begin by comparing conventional microscopy with digital pathology and outlining the major components of the digital pathology workflow. We then explore whole-slide imaging, its imaging modalities, scanner technologies, image storage, and image management systems. We then examine the role of artificial intelligence (AI) in digital pathology, discussing current AI applications and the spectrum of computational approaches, from conventional feature-engineering methods to deep learning and multimodal models. Finally, we review the clinical applications of digital pathology, summarize its implementation across US medical centers, and discuss the technical, regulatory, and operational challenges that must be addressed for broader clinical adoption.
SN - 2229-5089
SN - 2153-3539
ER -