Objective: Artificial intelligence (AI) is used in various urological conditions such as urolithiasis, pediatric urology, urogynecology, benign prostate hyperplasia (BPH), renal transplant, and uro-oncology. The various models of AI and its application in urology subspecialties are reviewed and discussed. Material and methods: Search strategy was adapted to identify and review the literature pertaining to the application of AI in urology using the keywords “urology,” “artificial intelligence,” “machine learning,” “deep learning,” “artificial neural networks,” “computer vision,” and “natural language processing” were included and categorized. Review articles, editorial comments, and non-urologic studies were excluded. Results: The article reviewed 47 articles that reported characteristics and implementation of AI in urologi-cal cancer. In all cases with benign conditions, artificial intelligence was used to predict outcomes of the surgical procedure. In urolithiasis, it was used to predict stone composition, whereas in pediatric urology and BPH, it was applied to predict the severity of condition. In cases with malignant conditions, it was applied to predict the treatment response, survival, prognosis, and recurrence on the basis of the genomic and biomarker studies. These results were also found to be statistically better than routine approaches. Application of radiomics in classification and nuclear grading of renal masses, cystoscopic diagnosis of bladder cancers, predicting Gleason score, and magnetic resonance imaging with computer-assisted diagnosis for prostate cancers are few applications of AI that have been studied extensively. Conclusions: In the near future, we will see a shift in the clinical paradigm as AI applications will find their place in the guidelines and revolutionize the decision-making process.
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