Simulation-based training in minimally invasive surgical therapies (MIST): current evidence and future directions for artificial intelligence integration—a systematic review by EAU endourology

Nedbal C. Gauhar V. Herrmann T. Singh A. Talyshinskii A. Al Jaafari F. Somani B.K.
December 2025 Springer Science and Business Media Deutschland GmbH

World Journal of Urology
2025 #43 Issue 1

Introduction: Benign 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 methods: A 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. Results: only 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. Conclusion: Simulation-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.

Artificial intelligence , BPH , MIST , Simulation , Training

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Polytechnic University Le Marche, Ancona, Italy
Urology, ASST Fatebenefratelli Sacco, Milan, Italy
Endourology Section, European Association of Urology, Arnhem, Netherlands
Urology, Ng Teng Fong General Hospital, Singapore, Singapore
Asian Institute of nephrourology (AINU), Nungambakkam, Chennai, India
Kantonspital FrauenfeldSpital Thurgau AG, Frauenfeld, Switzerland
Department of Community Medicine and Public Health, King George’s Medical University, Uttar Pradesh, Lucknow, India
Astana Medical University, Astana, Astana, Kazakhstan
School of Medicine, University of St Andrews and Victoria Hospital, NHS Fife, Kirkcaldy, United Kingdom
University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom

Polytechnic University Le Marche
Urology
Endourology Section
Urology
Asian Institute of nephrourology (AINU)
Kantonspital FrauenfeldSpital Thurgau AG
Department of Community Medicine and Public Health
Astana Medical University
School of Medicine
University Hospital Southampton NHS Foundation Trust

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