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Review
. 2023 Oct 3;18(1):109.
doi: 10.1186/s13000-023-01375-z.

Artificial intelligence in diagnostic pathology

Affiliations
Review

Artificial intelligence in diagnostic pathology

Saba Shafi et al. Diagn Pathol. .

Abstract

Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherein new tools such as digital imaging, advanced artificial intelligence (AI) algorithms, and computer-aided diagnostic techniques are being used for assisting, augmenting and empowering the computational histopathology and AI-enabled diagnostics. This is paving the way for advancement in precision medicine in cancer. Automated whole slide imaging (WSI) scanners are now rendering diagnostic quality, high-resolution images of entire glass slides and combining these images with innovative digital pathology tools is making it possible to integrate imaging into all aspects of pathology reporting including anatomical, clinical, and molecular pathology. The recent approvals of WSI scanners for primary diagnosis by the FDA as well as the approval of prostate AI algorithm has paved the way for starting to incorporate this exciting technology for use in primary diagnosis. AI tools can provide a unique platform for innovations and advances in anatomical and clinical pathology workflows. In this review, we describe the milestones and landmark trials in the use of AI in clinical pathology with emphasis on future directions.

Keywords: Algorithms; Artificial intelligence; Future; Pathology.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Milestones in digital and computational pathology with depiction of three “revolutions” in the field
Fig. 2
Fig. 2
Schematic representation of the how artificial intelligence (AI) can be applied in the practice of pathology
Fig. 3
Fig. 3
Depiction of artificial intelligence (AI) and machine learning approaches currently used by pathologists to analyze images from tumors

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