Artificial intelligence in muscle-invasive bladder cancer: opportunities, challenges, and clinical impact.

PURPOSE OF REVIEW

Muscle-invasive bladder cancer (MIBC) represents an aggressive malignancy with significant morbidity and mortality. Recent advances in artificial intelligence (AI) offer promising opportunities to enhance patient care across the entire MIBC management spectrum. This comprehensive review examines the current state and future potential of AI applications in MIBC, from diagnosis through treatment to response assessment.

RECENT FINDINGS

In the diagnostic domain, AI systems demonstrate superior accuracy in cystoscopic cancer detection and staging, with deep learning models achieving high performance in differentiating muscle-invasive from noninvasive disease. For treatment planning, AI facilitates precise tumor delineation for radiotherapy, automates adaptive planning, and supports surgical decision-making through predictive lymph node involvement models. In treatment response evaluation, machine learning algorithms show encouraging results in predicting neoadjuvant chemotherapy outcomes, while radiomics and quantitative imaging biomarkers enable early response assessment. Despite these advances, significant challenges persist, including methodological limitations, dataset heterogeneity, workflow integration barriers, and regulatory uncertainties. Future directions should prioritize prospective clinical validation, federated learning approaches to address data scarcity, development of interpretable AI models, and interdisciplinary collaboration.

SUMMARY

The integration of AI in MIBC management represents a paradigm shift toward personalized medicine, with the potential to improve diagnostic accuracy, optimize treatment selection, and enhance response prediction.