Evidence map›Paper›PMID 41800506›Full record

SynthesisThe Canadian journal of urology2026

Artificial intelligence assisted 3D in the robotic urooncology? A systematic review and narrative synthesis of current applications, challenges and future directions.

Bara Barakat, Bilal Al-Absi, Boris Hadaschik, Christian Rehme, Samer Schakaki, Joerg Bauer

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in The Canadian journal of urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Bara BarakatDepartment of Urology Robot-Assisted Urology and Uro-Oncology, Hospital Kassel, Kassel, 34125, Germany.
Bilal Al-AbsiDepartment of Urology Robot-Assisted Urology and Uro-Oncology, Hospital Kassel, Kassel, 34125, Germany.
Boris HadaschikDepartment of Urology, University Hospital Essen, Essen, 45147, Germany.
Christian RehmeDepartment of Urology, University Hospital Essen, Essen, 45147, Germany.
Samer SchakakiDepartment of Urology, Paracelsus Hospital Golzheim, Duesseldorf, 40474, Germany.
Joerg BauerDepartment of Urology Robot-Assisted Urology and Uro-Oncology, Hospital Kassel, Kassel, 34125, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial 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.

methodsA 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.

resultsTechnological 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.

conclusionAI appears to enhance robotic uro-oncologic procedures by increasing operative precision and supporting individualised surgical treatment strategies.

Indexed as

Artificial IntelligenceImaging, Three-DimensionalRobotic Surgical ProceduresUrologic Surgical ProceduresCystectomyHumansIntelligent SystemsNephrectomyProstatectomyartificial intelligenceautomatic three-dimensional surgical navigationdeep learningintuitive surgicalmachine learningrobot-assisted surgerysystematic review

Identifiers

PMID41800506

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.