Artificial intelligence and predictive tools in non-muscle invasive bladder cancer: a narrative review of current insights and advances.

Background and Objective: Non-muscle invasive bladder cancer (NMIBC) is characterized by high recurrence rates and heterogeneous progression risk, making accurate diagnosis, risk stratification, and personalized management challenging. Conventional clinical scoring systems provide general guidance but often fail to fully capture tumor complexity and interpatient variability. This review summarizes current applications of artificial intelligence (AI) in NMIBC, focusing on diagnosis, prognostic, and clinical decision-making. Methods: A comprehensive literature search was conducted in PubMed, Google Scholar, Embase and Scopus. Keywords related to AI and NMIBC including machine learning, deep learning, imaging, cystoscopy, radiomics, and computational pathology were used. Studies were independently screened, followed by full-text assessment for eligibility. A total of 35 English-language studies, published between January 2019 and March 2026, were included in the final qualitative synthesis. Key Content and Findings: AI applications in NMIBC span cystoscopy, imaging, histopathology, and prognostic modeling, demonstrating high diagnostic and predictive performance. In cystoscopy, deep learning models achieve sensitivities ranging from 88% to 97% and specificities from 92% to 99%, with area under the curves (AUCs) up to 0.98-0.99. Real-time segmentation reports Dice coefficients between 74% and 93%, with processing times approximately 6-7 ms per image. In imaging, AI-based radiomics and deep learning applied to magnetic resonance imaging (MRI) and computed tomography (CT) provide AUCs ranging from 0.82 to 0.99, often outperforming conventional models. Multiparametric MRI achieves AUCs of 0.88-0.91 for recurrence prediction, while CT-based models reach up to 0.997 for differentiating NMIBC from muscle-invasive disease. Prognostic models using machine learning, including random survival forests and neural networks, demonstrate improved discrimination compared to traditional scores, with concordance indices up to 0.79-0.88, enabling more granular risk stratification. In histopathology, AI-driven analysis of whole-slide images achieves accuracies of 74-90% for recurrence prediction and AUCs up to 0.86, while identifying patients at significantly higher risk of progression or treatment failure. Conclusions: AI enhances NMIBC management by enabling more precise, reproducible, and individualized diagnosis and risk assessment. The integration of multimodal data may improve clinical decision-making and support personalized treatment strategies, although further validation and standardization are required before widespread clinical implementation.