AB085. SOH25_AB_264. The role of artificial intelligence in urology: a systematic review

Background: Artificial intelligence (AI) is reshaping medical disciplines, including urology, by offering cutting-edge tools for diagnosis, treatment planning, and patient management. However, AI’s clinical utility, challenges, and future potential in urology remain underexplored. This systematic review aims to synthesize existing evidence, highlight current applications, and identify research gaps in this emerging field.

Methods: A comprehensive literature search was conducted across PubMed, Scopus, and IEEE Xplore databases. Eligible studies included those discussing AI applications in urology and surgery, focusing on machine learning, deep learning, imaging, robotic surgery, and predictive analytics. Data were extracted, assessed for quality using the PRISMA guidelines, and categorized into clinical and research applications.

Results: AI-assisted imaging improves diagnostic accuracy in prostate, kidney, and bladder cancer with sensitivities. Robotic-assisted surgery benefits from machine learning algorithms, enhancing precision and reducing complications. Predictive models for patient outcomes demonstrate promise but require external validation across diverse populations. Machine learning models aid in JJ stent follow-up by optimizing removal timing and reducing patient discomfort and healthcare costs. AI-driven platforms enhance outpatient care through automated symptom tracking, virtual follow-ups, and decision-support tools for urologists. Challenges include data heterogeneity, privacy concerns, and limited integration into real-world clinical workflows.

Conclusions: AI holds transformative potential for urology, particularly in oncology, surgical robotics, and personalized care. However, significant barriers, including standardization and validation, must be addressed to ensure safe and effective implementation. Future efforts should prioritize large-scale trials and interdisciplinary research to accelerate the adoption of AI in urology.

Keywords: Artificial intelligence (AI); urology; robotic surgery; machine learning; systematic review