Bladder cancer is one of the most prevalent malignant tumors of the urinary system worldwide, and its diagnosis and histopathological grading are crucial for clinical decision-making and prognostic evaluation. Although traditional methods such as cystoscopy, imaging, and histological examination remain the clinical gold standard, they suffer from significant subjectivity and interobserver variability. Artificial intelligence (AI), particularly deep learning (DL)–based approaches, has demonstrated substantial potential in image recognition, histopathological grading, and risk prediction. This review systematically summarizes recent advances in the application of AI to bladder cancer diagnosis and grading, covering imaging analysis, digital pathology, molecular marker identification, and AI-driven clinical decision support. In addition, key challenges associated with current AI technologies are discussed, including data quality, model generalizability, interpretability, clinical translation, and ethical and regulatory considerations. Finally, future research directions are outlined, including multimodal AI integration, incorporation of biomarkers, and the development of intelligent decision-support systems. Overall, AI is poised to play an increasingly important role in improving diagnostic accuracy and enabling personalized management of bladder cancer, thereby advancing the intelligent and data-driven management of urologic oncology.
Zhang et al. (2026) studied this question.