Literature DB >> 33965977

Artificial intelligence in prostate histopathology: where are we in 2021?

André Oszwald1, Gabriel Wasinger1, Benjamin Pradere2, Shahrokh F Shariat2, Eva M Compérat1.   

Abstract

PURPOSE OF REVIEW: Artificial intelligence has made an entrance into mainstream applications of daily life but the clinical deployment of artificial intelligence-supported histological analysis is still at infancy. Recent years have seen a surge in technological advance regarding the use of artificial intelligence in pathology, in particular in the diagnosis of prostate cancer. RECENT
FINDINGS: We review first impressions of how artificial intelligence impacts the clinical performance of pathologists in the analysis of prostate tissue. Several challenges in the deployment of artificial intelligence remain to be overcome. Finally, we discuss how artificial intelligence can help in generating new knowledge that is interpretable by humans.
SUMMARY: It is evident that artificial intelligence has the potential to outperform most pathologists in detecting prostate cancer, and does not suffer from inherent interobserver variability. Nonetheless, large clinical validation studies that unequivocally prove the benefit of artificial intelligence support in pathology are necessary. Regardless, artificial intelligence may soon automate and standardize many facets of routine work, including qualitative (i.e. Gleason Grading) and quantitative measures (i.e. portion of Gleason Grades and tumor volume). For the near future, a model where pathologists are enhanced by second-review or real-time artificial intelligence systems appears to be the most promising approach.
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

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Year:  2021        PMID: 33965977     DOI: 10.1097/MOU.0000000000000883

Source DB:  PubMed          Journal:  Curr Opin Urol        ISSN: 0963-0643            Impact factor:   2.309


  2 in total

1.  Defining AMIA's artificial intelligence principles.

Authors:  Anthony E Solomonides; Eileen Koski; Shireen M Atabaki; Scott Weinberg; John D McGreevey; Joseph L Kannry; Carolyn Petersen; Christoph U Lehmann
Journal:  J Am Med Inform Assoc       Date:  2022-03-15       Impact factor: 4.497

2.  Paper-based genetic assays with bioconjugated gold nanorods and an automated readout pipeline.

Authors:  Claudia Borri; Sonia Centi; Sofia Chioccioli; Patrizia Bogani; Filippo Micheletti; Marco Gai; Paolo Grandi; Serena Laschi; Francesco Tona; Andrea Barucci; Nicola Zoppetti; Roberto Pini; Fulvio Ratto
Journal:  Sci Rep       Date:  2022-04-13       Impact factor: 4.379

  2 in total

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