SynthesisWorld journal of urology2025
Simulation-based training in minimally invasive surgical therapies (MIST): current evidence and future directions for artificial intelligence integration-a systematic review by EAU endourology.
Synthesis in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Transforming thyroid disease education: AI and virtual technologies in residency training.Frontiers in endocrinology · 2026Review
- The Emerging Role of Multimodal Artificial Intelligence in Urological Surgery.Current oncology (Toronto, Ont.) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionBenign prostatic hyperplasia (BPH) affects a growing proportion of the aging male population. Minimally invasive surgical therapies (MISTs) such as Rezum and UroLift offer effective alternatives to traditional approaches like transurethral resection of the prostate (TURP). However, training in these procedures is challenged by limited case exposure and variability across residency programs. Simulation-based training has emerged as a valuable tool to enhance surgical education. This study aims to assess the current evidence on simulation-based training for Rezum and UroLift, evaluating its validity, effectiveness, and potential integration with artificial intelligence (AI) in urology education. MATERIALS AND
methodsA systematic literature review was conducted on March 11, 2025, across PubMed, Scopus, Cochrane, and Google Scholar following PRISMA guidelines. Search terms included combinations of MIST techniques (Rezum, UroLift, iTIND) and training modalities (simulation, virtual reality, artificial intelligence). Studies were selected using PICOS criteria, focusing on urology trainees undergoing simulation-based training. Preclinical, review, and non-English studies were excluded.
resultsonly 3 studies met the inclusion criteria: one focused on Ron between junior and senior residents, especially in implant placement and procedural technique. Simulation was highly rated by trainees in workshop settings, though predictive validity remains unproven.
conclusionSimulation-based training for Rezum and UroLift is a promising method to enhance resident competency in MIST procedures. Current evidence supports its face, content, and construct validity, though further studies are needed to confirm predictive validity and optimize training protocols. Integration of AI and telementoring may further improve training effectiveness and accessibility across institutions.
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Registered trials
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.