Artificial intelligence in medicine has allowed for efficient processing of large datasets to perform cognitive tasks that facilitate clinical decision-making, and it is an emerging area of research. This review aims to highlight the most pertinent and recent research in artificial intelligence in endourology, where it has been used to optimize stone diagnosis, support decision-making regarding management, predict stone recurrence, and provide new tools for bioinformatics research within endourology. Artificial neural networks (ANN) and machine learning approaches have demonstrated high accuracy in predicting stone diagnoses, stone composition, and outcomes of spontaneous stone passage, shockwave lithotripsy (SWL), or percutaneous nephrolithotomy (PCNL); some of these models outperform more traditional predictive models and existing nomograms. In addition, these approaches have been used to predict stone recurrence, quality of life scores, and provide novel methods of mining the electronic medical record for research. Artificial intelligence can be used to enhance existing approaches to stone diagnosis, management, and prevention to provide a more individualized approach to endourologic care. Moreover, it may support an emerging area of bioinformatics research within endourology. However, despite high accuracy, many of the published algorithms lack external validity and require further study before they are more widely adopted.
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