This comprehensive review critically examines the transformative impact of artificial intelligence (AI) and radiomics in the diagnosis, prognosis, and management of bladder, kidney, and prostate cancers. These cutting-edge technologies are revolutionizing the landscape of cancer care, enhancing both precision and personalization in medical treatments. Our review provides an in-depth analysis of the latest advancements in AI and radiomics, with a specific focus on their roles in urological oncology. We discuss how AI and radiomics have notably improved the accuracy of diagnosis and staging in bladder cancer, especially through advanced imaging techniques like multiparametric MRI (mpMRI) and CT scans. These tools are pivotal in assessing muscle invasiveness and pathological grades, critical elements in formulating treatment plans. In the realm of kidney cancer, AI and radiomics aid in distinguishing between renal cell carcinoma (RCC) subtypes and grades. The integration of radiogenomics offers a comprehensive view of disease biology, leading to tailored therapeutic approaches. Prostate cancer diagnosis and management have also seen substantial benefits from these technologies. AI-enhanced MRI has significantly improved tumor detection and localization, thereby aiding in more effective treatment planning. The review also addresses the challenges in integrating AI and radiomics into clinical practice, such as the need for standardization, ensuring data quality, and overcoming the “black box” nature of AI. We emphasize the importance of multicentric collaborations and extensive studies to enhance the applicability and generalizability of these technologies in diverse clinical settings. In conclusion, AI and radiomics represent a major paradigm shift in oncology, offering more precise, personalized, and patient-centric approaches to cancer care. While their potential to improve diagnostic accuracy, patient outcomes, and our understanding of cancer biology is profound, challenges in clinical integration and application persist. We advocate for continued research and development in AI and radiomics, underscoring the need to address existing limitations to fully leverage their capabilities in the field of oncology.

With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included “urologic surgery”, “artificial intelligence”, “machine learning”, “neural network”, “automation”, and “robotic surgery”. Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ‘’master–slave’’ robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments.

Robotic surgery has become a cornerstone of modern urologic practice, with the da Vinci system maintaining dominance for over two decades. In recent years, however, a new generation of robotic platforms has emerged, introducing greater competition and innovation into the field. These systems aim to address unmet needs through features such as modular architectures, enhanced ergonomics, haptic feedback, and cost-containment strategies. Several platforms—including Hugo™ RAS, Versius™, Avatera™, REVO-I, Hinotori™, Senhance™, KangDuo, MicroHand S, Dexter™, and Toumai^®—have entered clinical use with early results demonstrating perioperative and short-term oncologic outcomes broadly comparable to those of established systems, particularly in procedures such as radical prostatectomy, partial nephrectomy, and radical cystectomy. At the same time, they introduce unique advantages in workflow flexibility, portability, and economic feasibility. Nevertheless, important challenges remain, including the need for rigorous comparative trials, standardized training curricula, and long-term cost-effectiveness analyses. The integration of artificial intelligence, augmented reality, and telesurgery holds the potential to further expand the role of robotics in urology, offering opportunities to enhance precision, improve accessibility, and redefine perioperative care models. This review summarizes the evolving landscape of robotic platforms in urology, highlights their clinical applications and limitations, and outlines future directions for research, training, and global implementation.

The evolution of robotic surgery in urologic oncology has led to the emergence of single-port (SP) robotic systems as a potential alternative to the widely adopted multi-port (MP) platforms. This narrative review provides a comprehensive comparison between SP and MP robotic systems, the former of which received FDA approval in 2018 and CE marking in 2024, focusing on their application across radical prostatectomy, partial and radical nephrectomy, and radical cystectomy. Drawing from the most current literature, we examine perioperative outcomes, oncologic efficacy, postoperative recovery, and complication rates. The review highlights the technical challenges unique to SP surgery, including restricted triangulation, limited instrumentation, and a defined learning curve, while also emphasizing innovations such as transvesical prostatectomy and the Supine Anterior Retroperitoneal Access (SARA) approach. Additionally, we explore the potential impact of emerging technologies—such as artificial intelligence, augmented reality, and telesurgery—on the future of SP platforms. Despite early limitations, SP systems have demonstrated comparable safety and effectiveness in selected cases and may offer unique advantages in specific anatomical scenarios. Continued innovation, structured training, and robust long-term outcome data will be essential for the broader adoption and integration of SP robotic surgery in clinical practice.

Muscle-invasive bladder cancer (MIBC) is a biologically aggressive disease with high recurrence rates, despite advances in surgical and systemic therapies. Circulating tumor DNA (ctDNA), a tumor-specific fraction of cell-free DNA, has emerged as a promising non-invasive biomarker for the real-time assessment of tumor burden, treatment response, and minimal residual disease (MRD). This review explores the biological basis, detection technologies, and clinical utility of ctDNA in MIBC, highlighting its role in preoperative risk stratification, postoperative surveillance, and personalized decision-making for adjuvant and systemic therapies. We critically examine current evidence from pivotal trials and ongoing studies that support ctDNA’s prognostic and predictive value. Additionally, we discuss emerging applications, including ctDNA-guided immunotherapy, integration with imaging and molecular data, and potential to inform bladder-sparing strategies. While ctDNA presents technical and logistical challenges, its incorporation into prospective clinical workflows promises to enhance precision oncology and improve outcomes in patients with MIBC.