Patient centered care for prostate cancer—how can artificial intelligence and machine learning help make the right decision for the right patient?

In the era of advancing technologies, large volumes of data are being collected by electronic medical records and clinical registries are readily available for data mining (1). These registries, however, require appropriate analyses and interpretation to derive clinically meaningful benefits for patients. Traditional statistical models have been previously used for this purpose. However, they are incapable of processing highly dimensional data and do not actively adapt with the incorporation of new data points. Although information from clinical registries assist physicians in making data-driven decisions, there are limited opportunities for patients to directly interact with these registries to help them make informed decisions.