Evidence map›Paper›PMID 40476270›Full record

ArticleEuropean urology open science2025

Harnessing Advanced Imaging Technologies in Robotic Urological Surgery Requires Specialized and Structured Training.

Enrico Checcucci, Stefano Puliatti, Gabriele Volpi, Gabriele Bignante, Francesco Porpiglia, Riccardo Campi, Daniele Amparore

Abstract readLetter
In one paragraph

Article in European urology open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. The times they are (still) a-Changin'.Prostate cancer and prostatic diseases · 2026
    Article
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

7 authors.

Enrico CheccucciDepartment of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy.
Stefano PuliattiDepartment of Urology, University of Modena and Reggio Emilia, Modena, Italy.
Gabriele VolpiDepartment of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy.
Gabriele BignanteDepartment of Oncology, Division of Urology, University of Turin, San Luigi Gonzaga Hospital, Orbassano (TO), Italy.
Francesco PorpigliaDepartment of Oncology, Division of Urology, University of Turin, San Luigi Gonzaga Hospital, Orbassano (TO), Italy.
Riccardo CampiDepartment of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
Daniele AmparoreDepartment of Oncology, Division of Urology, University of Turin, San Luigi Gonzaga Hospital, Orbassano (TO), Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, the integration of advanced technologies in surgery has transformed health care, with urology consistently pioneering these innovations. Unlike the structured drug development process, surgical advancements follow a distinct pathway, exemplified by the Innovation, Development, Exploration, Assessment, and Long-term follow-up (IDEAL) framework, which supports safe, evidence-based innovation. Complementary technologies, including three-dimensional (3D) models and augmented reality (AR), have emerged as transformative tools in robotic urological surgery, particularly for complex cases. These technologies enhance surgical planning, intraoperative navigation, and postoperative outcomes, especially in kidney and prostate cancer surgeries. Despite growing adoption, structured training programs tailored to 3D and AR applications remain underdeveloped. To address this gap, we conducted an online survey of participants from the 12th Techno Urology Meeting (2024) to evaluate the current use and training needs for these technologies. Among 126 respondents, the majority utilized 3D models (64.5%) and AR (40.3%) in clinical practice, highlighting their role in robotic partial nephrectomy and radical prostatectomy. Respondents overwhelmingly emphasized the need for dedicated multidisciplinary courses with hands-on training and proficiency-based progression methodologies. This study underscores the necessity of structured, technology-specific training to maximize the potential of 3D models and AR in robotic urology, offering critical insights for advancing surgical education and improving patient care outcomes. Patient summary: This study highlights the increasing integration of advanced technologies such as three-dimensional models and augmented reality in urological surgery, and emphasizes the growing demand for proper training to maximize their potential. While demonstration of their direct impact on surgical precision and outcomes would require a different study design, this analysis primarily underscores the need to establish comprehensive training programs to ensure safer and more effective care for patients.

Indexed as

Augmented realityRobotic surgeryThree-dimensional modelsTraining

Identifiers

PMID40476270
PMCPMC12139682

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.