Shaping the Future of Personalized Therapy in Bladder Cancer Using Artificial Intelligence

Bladder cancer (BC) ranks among the tenth most common cancers globally, and its management remains a significant challenge for both patients and clinicians in terms of care delivery and decision-making process. The integration of artificial intelligence (AI) tools-primarily machine learning and deep learning methods-into the current BC workflow offers an opportunity for a more personalized approach to treatment. This article provides a brief overview of AI applications across different steps of BC management (ie, detection, grading, staging, risk stratification, treatment, and outcome prediction), highlighting its potential to contribute to individualized management strategies. Despite significant advances, major barriers still impede broad applications of AI in BC clinical workflows. Overcoming these obstacles is critical to realize the full potential of AI-driven personalization of BC care in the coming decade. PATIENT SUMMARY: Our mini review summarizes how artificial intelligence (ie, a machine's ability to mimic human intelligence to perform tasks involving decision-making and problem-solving) has been applied to the management of bladder cancer, and whether it could lead to more precise treatment for patients diagnosed with this disease. Although several promising applications have been developed, more studies are necessary before these can be used in routine clinical practice.