Integrating artificial intelligence across the bladder cancer continuum: progress, promise, and pitfalls

Bladder cancer is a prevalent and costly malignancy, with persistent challenges in early detection, accurate staging, and personalized treatment planning. Artificial intelligence (AI) has emerged as a transformative tool with the potential to address these limitations across the bladder cancer continuum.

This review synthesizes findings from 49 studies selected through a comprehensive literature search of PubMed, MEDLINE, Embase, Scopus, and Google Scholar spanning 2005 to 2025. The included studies explore AI applications in cystoscopic lesion detection, radiologic staging using CT and MRI, histopathologic grading, molecular biomarker profiling, treatment response prediction, and survival prognostication.

AI has demonstrated significant promise in enhancing diagnostic precision, reducing interobserver variability, and enabling individualized treatment strategies. However, widespread clinical adoption remains limited due to challenges in data quality, lack of multicenter validation, integration into electronic health records, and regulatory hurdles. Future research should prioritize explainable AI models, prospective validation, and demonstration of cost-effectiveness and survival benefits. With continued innovation and standardization, AI is poised to become an integral component of precision oncology in bladder cancer care.