Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 Nov 15;12(11):2794.
doi: 10.3390/diagnostics12112794.

Application of Artificial Intelligence in Pathology: Trends and Challenges

Inho Kim  1 , Kyungmin Kang  1 , Youngjae Song  1 , Tae-Jung Kim  2
Affiliations
Review

Application of Artificial Intelligence in Pathology: Trends and Challenges

Inho Kim et al. Diagnostics (Basel). .

Abstract

Given the recent success of artificial intelligence (AI) in computer vision applications, many pathologists anticipate that AI will be able to assist them in a variety of digital pathology tasks. Simultaneously, tremendous advancements in deep learning have enabled a synergy with artificial intelligence (AI), allowing for image-based diagnosis on the background of digital pathology. There are efforts for developing AI-based tools to save pathologists time and eliminate errors. Here, we describe the elements in the development of computational pathology (CPATH), its applicability to AI development, and the challenges it faces, such as algorithm validation and interpretability, computing systems, reimbursement, ethics, and regulations. Furthermore, we present an overview of novel AI-based approaches that could be integrated into pathology laboratory workflows.

Keywords: artificial intelligence; computational pathology; deep learning; digital pathology; histopathology image analysis.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Embedding AI into pathology department workflow. The digital pathology supplies whole-slide images to artificial intelligence, which performs quality control of pre-analytic phase, analytic phase and post-analytic phase of pathology laboratory process.
Figure 2
Figure 2
Requirement for clinical applications of artificial intelligence with CPATH.
Figure 3
Figure 3
Images are divided into small patches obtained from tissue of WSI, which are subsequently prepared to have semantic features extracted from each patch. Green tiles indicate tumor region; red tiles indicate non-tumor region. Images from Yeouido St. Mary’s hospital.
Figure 4
Figure 4
(a) Random sampling of 100 patches selected arbitrarily from an WSI image. (b) Random sampling of 100 patches after application of Laplace filter (which highlights areas with great changes) from WSI image. Images from Yeouido St. Mary’s Hospital.
See this image and copyright information in PMC

References

    1. Gurcan M.N., Boucheron L.E., Can A., Madabhushi A., Rajpoot N.M., Yener B. Histopathological image analysis: A review. IEEE Rev. Biomed. Eng. 2009;2:147–171. doi: 10.1109/RBME.2009.2034865. - DOI - PMC - PubMed
    1. Wolberg W.H., Street W.N., Heisey D.M., Mangasarian O.L. Computer-derived nuclear features distinguish malignant from benign breast cytology. Hum. Pathol. 1995;26:792–796. doi: 10.1016/0046-8177(95)90229-5. - DOI - PubMed
    1. Choi H.K., Jarkrans T., Bengtsson E., Vasko J., Wester K., Malmström P.U., Busch C. Image analysis based grading of bladder carcinoma. Comparison of object, texture and graph based methods and their reproducibility. Anal. Cell. Pathol. 1997;15:1–18. doi: 10.1155/1997/147187. - DOI - PMC - PubMed
    1. Keenan S.J., Diamond J., Glenn McCluggage W., Bharucha H., Thompson D., Bartels P.H., Hamilton P.W. An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN) J. Pathol. 2000;192:351–362. doi: 10.1002/1096-9896(2000)9999:9999<::AID-PATH708>3.0.CO;2-I. - DOI - PubMed
    1. Weinstein R.S., Graham A.R., Richter L.C., Barker G.P., Krupinski E.A., Lopez A.M., Erps K.A., Bhattacharyya A.K., Yagi Y., Gilbertson J.R. Overview of telepathology, virtual microscopy, and whole slide imaging: Prospects for the future. Hum. Pathol. 2009;40:1057–1069. doi: 10.1016/j.humpath.2009.04.006. - DOI - PubMed

LinkOut - more resources