The Application and Research Progress of Artificial Intelligence in Urologic Oncology: A Comprehensive

Artificial Intelligence (AI) technologies, particularly branches represented by Machine Learning (ML) and Deep Learning (DL), are impacting the medical field with unprecedented speed and depth, heralding a significant revolution in the clinical management and scientific research of urologic oncology. Urologic tumors, including prostate cancer, bladder cancer, upper tract urothelial carcinoma, and renal cancer, pose a major challenge to global public health due to their high incidence and mortality rates. This review aims to systematically summarize and discuss the application of AI in the screening, early detection, and precision diagnosis of these major urologic malignancies, with a special focus on medical image interpretation and pathological assessment. Furthermore, it explores AI's role in tumor grading and staging, the construction of prognostic models, and the selection and optimization of personalized treatment strategies. Specifically, this paper will detail AI's function in interpreting multiparametric MRI (mpMRI), CT urography, endoscopic imaging, and digital pathology slides, examining its contributions to improving diagnostic accuracy, enabling non-invasive evaluation, and optimizing risk stratification. This review also analyzes the key bottlenecks currently facing AI applications in this field, such as data quality and sharing, model interpretability and generalization, the complexity of clinical validation, algorithmic bias, and ethical and regulatory challenges. It also outlines future directions, including multi-modal data fusion, advancements in explainable AI, federated learning, and the role of AI in robotic surgery and drug discovery, striving to provide a comprehensive and in-depth reference for clinicians and researchers.