Machine learning algorithms can more efficiently predict biochemical recurrence after robot-assisted radical prostatectomy.

The construction of more reliable and potent models will provide the clinicians and patients with advantages such as more accurate risk classification, prognosis estimation, early intervention, avoidance of unnecessary treatments, relatively lower morbidity and mortality. The ML methods are cheap, and their powers increase with increasing data input; we believe that their clinical use will increase over time.