The Role of AI in Enhancing Precision Medicine for Urological Cancer: A Systematic Review

Abstract

Background: The integration of artificial intelligence (AI) with precision medicine represents a promising frontier in urological cancer management. This systematic review evaluates the current landscape of AI applications in precision medicine for urological cancers, analyzing methodological approaches, clinical applications, and implementation challenges.
Methods: A comprehensive literature search was conducted across PubMed/MEDLINE, Embase, Web of Science, and IEEE Xplore databases from January 2015 to December 2024. The review followed PRISMA guidelines, focusing on original research articles exploring AI applications in precision medicine for urological cancers. Quality assessment was performed using QUADAS-2 and ROBINS-I tools, with AI-specific evaluation using AI-RADS criteria.
Results: Among 2,847 identified articles, 89 studies met the inclusion criteria. Prostate cancer studies dominated the literature (47.2%), followed by bladder (28.1%), kidney (20.2%), and testicular cancer (4.5%). Deep learning approaches were most prevalent (42.7%), achieving the highest performance metrics in prostate cancer applications (accuracy 88.5%, AUC-ROC 0.91). External validation was reported in 50.6% of studies, with multi-institutional validation in 31.5%. Implementation challenges were identified in 75.3% of studies, primarily concerning data quality (77.6%) and workflow integration (71.6%).
Conclusion: AI applications in precision medicine for urological cancers demonstrate promising performance metrics and potential for clinical impact. However, the field faces significant challenges in data standardization, external validation, and clinical integration. Future developments should focus on multi-institutional collaboration, standardized validation protocols, and improved implementation strategies to enhance the clinical utility of AI-driven precision medicine approaches in urological oncology