Keywords
digital pathology, whole slide imaging (WSI), whole slide image scanners, artificial intelligence (AI), pathology AI, computational pathology
Abstract
The adoption of whole slide image (WSI) scanners in clinical practice was accelerated by US Food and Drug Administration approval in 2017, which allowed primary pathologic diagnoses to be made on scanned images. Images in the digital domain allow the application of pathology artificial intelligence (AI), including clinical decision support with algorithms performing specific diagnoses.1,2 These algorithms, if trained properly, could go beyond the ability of human observation to detect and quantify features that are not recognizable by human perception.1,3,4
Original Publication Citation
Frewing A, Gibson AB, Robertson R, Urie PM, Corte DD. Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology. Archives of Pathology & Laboratory Medicine. 2024 Apr;148(5):603–612. doi:10.5858/arpa.2022-0460-RA. https://doi.org/10.5858/arpa.2022-0460-RA
BYU ScholarsArchive Citation
Frewing, Aaryn; Gibson, Alexander B.; Robertson, Richard; Urie, Paul; and Della Corte, Dennis, "Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology" (2023). Faculty Publications. 9547.
https://scholarsarchive.byu.edu/facpub/9547
Document Type
Peer-Reviewed Article
Publication Date
2023-08-18
Publisher
Archives of Pathology & Laboratory Medicine
Language
English
College
Computational, Mathematical and Physical Sciences
Department
Physics and Astronomy
Copyright Use Information
https://lib.byu.edu/about/copyright/
Included in
Artificial Intelligence and Robotics Commons, Bioimaging and Biomedical Optics Commons, Medical Pathology Commons