Digital twins in urology: a vision for the future of urological practice

TY - JOUR

T1 - Digital twins in urology

T2 - a vision for the future of urological practice

AU - Nedbal, Carlotta

AU - Madaan, Sanjeev

AU - Nabi, Ghulam

AU - Somani, Bhaskar Kumar

N1 - Publisher Copyright: © The Author(s), 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

PY - 2026/2/10

Y1 - 2026/2/10

N2 - 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.

AB - 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.

KW - artificial intelligence

KW - digital twins

KW - machine learning

KW - oncology

KW - personalized medicine

KW - urology

UR - https://www.scopus.com/pages/publications/105040207055

U2 - 10.1177/17562872261419592

DO - 10.1177/17562872261419592

M3 - Review article

C2 - 41684950

AN - SCOPUS:105040207055

SN - 1756-2872

JO - Therapeutic Advances in Urology

JF - Therapeutic Advances in Urology

ER -