AI and Machine Learning in Urology: Current Uses and Future Directions - Sriram, Aadhitya; Sriram, Shreenidhi; Ramachandran, Kalpana; Krishnamoorthy, Sriram (Science and Technology: Developments and Applications Vol. 8, 2025)
Background: Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed modern urology by enhancing diagnostic accuracy, surgical precision, and patient management. AI-driven innovations are increasingly integrated into urological practice, enabling early disease detection, predictive analytics, risk stratification, and robotic-assisted surgeries. This paper explores the current landscape of AI in urology, analyzing its applications in diagnostics, treatment planning, and surgical interventions. It highlights AI-driven technologies' benefits, challenges, and future research directions in optimizing patient care. Methods: A comprehensive literature review was conducted on AI applications in urology, examining studies on machine learning models—including deep learning and reinforcement learning—for detecting prostate, kidney, and bladder cancer, predictive analytics for disease progression, and AI-enhanced robotic surgeries. The analysis encompasses regulatory considerations, ethical implications (including data bias and patient privacy concerns), and real-world applications of AI in clinical settings. Results: AI performs superiorly in diagnostic imaging, histopathological analysis, and personalized treatment recommendations. Machine learning models enhance risk stratification, enabling more targeted therapeutic approaches. AI-driven robotic surgical systems enhance precision and reduce complications, while AI-powered remote monitoring tools optimize postoperative care. However, data bias, interpretability, regulatory constraints, and ethical concerns hinder widespread adoption. Conclusion: AI is revolutionizing urology by improving efficiency, accuracy, and patient outcomes. Future advancements in AI-driven precision medicine, autonomous robotic surgery, and AI-integrated telemedicine are promising. Addressing challenges related to data privacy, bias mitigation, and regulatory approval will be crucial for the seamless integration of AI into urological practice. Continued research and interdisciplinary collaboration will enhance AI's role in transforming urological healthcare.