LAUSR

Bladder cancer (BCa) is the most common malignancy of the urinary tract and the most expensive malignancy to treat over the patients' lifetime. In recent years a number of studies have utilized Artificial Intelligence (AI) algorithms to perform certain clinical tasks involved in BCa diagnosis and outcome prediction. These tasks include automatic tumor detection, staging, and grading, bladder wall segmentation, as well as prediction of recurrence, response to chemotherapy, and overall survival. Despite the promising results reported, AI algorithms have not been fully integrated into the clinical workflow. In this article we (1) provide an accessible introduction to the fundamental nomenclature and concepts in AI, (2) review the literature to explore how AI is used for BCa diagnosis and outcome prediction, and (3) present our perspective on the obstacles that must be removed before AI algorithms can enter the mainstream of cancer management.