Application of Artificial Intelligence in Pathology: Trends and Challenges

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 intellig...

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Bibliographic Details
Published in:Diagnostics (Basel) 2022-11, Vol.12 (11), p.2794
Main Authors: Kim, Inho, Kang, Kyungmin, Song, Youngjae, Kim, Tae-Jung
Format: Article
Language:English
Subjects:
Algorithms
Archives & records
Artificial intelligence
Automation
Biomarkers
Cancer
computational pathology
Deep learning
Digital imaging
digital pathology
Digitization
Forecasts and trends
Health aspects
histopathology image analysis
Laboratories
Machine learning
Medical prognosis
Metadata
Microscopy
Pathology
Quality control
Review
Software
Technology application
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Summary: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.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics12112794