TY - JOUR T1 - Future Directions: Artificial Intelligence and Digital Tools in Bladder Cancer Care. AU - Jiang,Tommy, AU - Zhao,Calvin C, AU - Liao,Joseph C, Y1 - 2026/04/27/ PY - 2026/6/27/medline PY - 2026/6/27/pubmed PY - 2026/6/26/entrez KW - Artificial intelligence KW - Bladder cancer KW - Diagnostics KW - Therapeutics SP - 497 EP - 508 JF - The Urologic clinics of North America JO - Urol Clin North Am VL - 53 IS - 3 N2 - This comprehensive review examines using artificial intelligence (AI) across the diagnostic, therapeutic, and prognostic landscape of bladder cancer. It highlights AI's capacity to enhance tumor detection, histopathologic interpretation, surgical precision, and personalized treatment planning through machine learning and computer vision. Applications such as AI-assisted cystoscopy, cytology, transurethral resection of bladder tumor optimization, and prognostic modeling demonstrate significant potential to reduce variability and improve clinical decision-making. While early evidence is promising, widespread implementation requires rigorous validation, multicenter collaboration, and regulatory standardization. Ultimately, AI represents a paradigm shift toward data-driven, precision-based bladder cancer management which can potentially transform patient outcomes globally. SN - 1558-318X UR - https://www.unboundmedicine.com/prime/citation/42362307/Future_Directions:_Artificial_Intelligence_and_Digital_Tools_in_Bladder_Cancer_Care. DB - PRIME DP - Unbound Medicine ER -