Open Access
REVIEW
Artificial intelligence assisted 3D in the robotic urooncology? A systematic review and narrative synthesis of current applications, challenges and future directions
Bara Barakat1,*, Bilal Al-Absi1, Boris Hadaschik2, Christian Rehme2, Samer Schakaki3, Joerg Bauer1
1 Department of Urology Robot-Assisted Urology and Uro-Oncology, Hospital Kassel, Kassel, 34125, Germany
2 Department of Urology, University Hospital Essen, Essen, 45147, Germany
3 Department of Urology, Paracelsus Hospital Golzheim, Duesseldorf, 40474, Germany
* Corresponding Author: Bara Barakat. Email: 
Canadian Journal of Urology https://doi.org/10.32604/cju.2026.071284
Received 04 August 2025; Accepted 26 November 2025; Published online 29 January 2026
Abstract
Background: Artificial intelligence (AI)-assisted three-dimensional (3D) surgical platforms, integrated with augmented reality, have the potential to improve intraoperative anatomical recognition and provide surgeons with an immersive, dynamic operating environment during uro-oncological procedures. This review aims to examine the current applications of AI in robotic uro-oncology, with a particular focus on its role in facilitating intraoperative navigation during complex surgeries. Methods: A systematic literature search was performed across PubMed, the National Library of Medicine, MEDLINE, the Cochrane Central Register of Controlled Trials (CENTRAL), ClinicalTrials.gov, and Google Scholar to identify relevant studies published up to July 2025. The search strategy incorporated a predefined set of keywords, including AI, machine learning, radical prostatectomy (RP), robotic-assisted radical prostatectomy (RARP), robot-assisted partial nephrectomy (RAPN), and robot-assisted radical cystectomy (RARC). Only clinical trials, full-text peer-reviewed publications, and original research articles were included. Studies were eligible for inclusion if they evaluated or described applications of AI in RARP, RAPN, or RARC. Results: Technological advancements have substantially transformed the field of uro-oncologic surgery. In particular, AI and AI-assisted intraoperative navigation in RARP demonstrate considerable potential to objectively assess surgical performance and predict clinical outcomes. In RAPN, the adoption of preoperative, interactive 3D virtual models for surgical planning has influenced surgical decisions, thus, enhanced precision in resection planning correlates with superior nephron-sparing outcomes and optimized selective clamping. AI applications in RARC, techniques such as augmented reality (AR) can overlay critical information on the surgical field, by facilitating navigation through complex anatomical planes and enhancing identification of critical structures. Conclusion: AI appears to enhance robotic uro-oncologic procedures by increasing operative precision and supporting individualised surgical treatment strategies.
Keywords
artificial intelligence; robot-assisted surgery; machine learning; deep learning; automatic three-dimensional surgical navigation; intuitive surgical; systematic review
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