Artificial intelligence in prostate histopathology: where are we in 2021?. Issue 4 (July 2021)

• Record Type: Journal Article
• Authors: Oszwald, André; Wasinger, Gabriel; Pradere, Benjamin; Shariat, Shahrokh F.; Compérat, Eva M.
• Is Part Of: Current opinion in urology. Volume 31:Issue 4(2021)
• Publication Date: 2021-07
• Subjects: artificial intelligence -- Gleason score -- histology -- pathology -- prostate cancer; Urology -- Periodicals; Review Literature -- Bibliography; Review Literature -- Periodicals; Male Urogenital Diseases -- Bibliography; Male Urogenital Diseases -- Periodicals; Female Urogenital Diseases -- Bibliography; Female Urogenital Diseases -- Periodicals; Urologic Diseases -- Bibliography; Urologic Diseases -- Periodicals; Periodicals; 616.6005
• DOI: 10.1097/MOU.0000000000000883
• Languages: English
• ISSNs: 1473-6586

Abstract

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.