Current status of artificial intelligence applications in Urology and its potential to influence clinical practice.

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Abstract

With an increase in sources, volume and availability of big data, clinicians face new challenges in synthesizing information in order to provide individualized patient care. To address these challenges, artificial intelligence (AI) has been broadly used in medicine. AI techniques, such as computer vision, machine learning, artificial neural networks, deep learning, are complex mathematical algorithms mimicking human neuronal structure and ways of thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. AI technology has been applied in various aspects of urologic diseases, including utilizing imaging radiomic features to improve or automate cancer diagnosis or predict disease recurrence, digitized tissue specimen images to automate detection of cancer on pathology slides and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies utilized AI to plan brachytherapy and radiation for prostate cancer while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistics, a well-constructed AI has the advantage of improved accuracy in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence-based and individualized patient care. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.