Digital twins, dynamic, patient-specific virtual replicas of physical systems are gaining momentum across various medical fields. Their capacity to simulate and predict biological behavior offers unprecedented opportunities for personalized care. While their utility has been demonstrated in oncology, their application in urology remains nascent, and there is still a significant gap in literature findings. This review explores the theoretical framework, current evidence, and future potential of digital twins technology in managing urological conditions such as urolithiasis, benign prostatic hyperplasia (BPH), and nonmuscle invasive bladder cancer. A narrative literature review, guided by SANRA criteria, was conducted using PubMed and Scopus databases to identify relevant studies on the application of digital twins in healthcare, with a focus on urology and related fields. Additional sources from AI, imaging, and computational modelling were incorporated to support technological and theoretical concepts. Real-world use cases and clinical scenarios were constructed to illustrate the conceptual application of digital twins in endourology. Although limited in number, preliminary studies have shown promising results in uro-oncology, particularly for prostate and renal cancers. No models have yet been developed for benign urological diseases. Hypothetical applications in endourology include personalized procedural planning, dynamic follow-up, prediction of obstruction or recurrence, and real-time intraoperative guidance. Integration of imaging, functional data, and AI algorithms could create continuously adaptive simulations to support decision-making and improve treatment outcomes. However, barriers such as data complexity, cost, lack of validation frameworks, and clinician trust persist. Digital twins hold transformative potential for the future of urology, promising to personalize care across diagnosis, treatment, and surveillance. While the path to clinical integration is challenged by technical, ethical, and infrastructural hurdles, collaborative efforts and real-world validation studies are critical to realizing their full potential in clinical practice.
Nedbal et al. (Sun,) studied this question.