Surgical proficiency of trainee surgeons on robotic platforms: a systematic review and meta-analysis.

Robotic-assisted surgery has transformed minimally invasive procedures by enhancing precision, dexterity, and visualization. However, it presents unique psychomotor and cognitive challenges that traditional surgical training often fails to address. Simulation-based platforms such as the da Vinci Skills Simulator (dVSS) and SimNow are widely implemented, yet a universally accepted standard for assessing robotic surgical proficiency remains elusive. Commonly used evaluation tools-GEARS, OSATS, and expert assessments-vary in standardization and applicability. To systematically review and quantitatively synthesize current evidence on simulation-based robotic surgical training in trainees, focusing on proficiency assessment tools, learning curve thresholds, and skill transfer from conventional surgical techniques. A systematic search of five databases (PubMed, Embase, Scopus, Web of Science, and Cochrane Library) was performed up to June 2025. Eligible studies included trainee populations assessed using validated robotic simulators or evaluation metrics. Data extraction followed PRISMA 2020 guidelines. Pooled effect sizes were calculated using a random-effects meta-analysis. Fourteen studies including 652 surgical trainees met the inclusion criteria. Simulation-based training significantly improved performance on GEARS (SMD 1.22, 95% CI 0.96-1.49) and OSATS (SMD 1.08, 95% CI 0.82-1.34). Task completion times (SMD -0.95) and error rates (SMD-1.03) also improved markedly. Learning curve analyses revealed performance plateaus between 15 and 35 sessions (median: 22). Subgroup analyses showed comparable efficacy between dVSS and SimNow simulators. Skill transfer from laparoscopic surgery showed a moderate effect (SMD ~ 0.40), while no significant benefit was observed from open surgical experience. The integration of expert-defined proficiency thresholds and real-time feedback accelerated skill acquisition and improved self-efficacy scores. Simulation-based training significantly enhances robotic surgical proficiency, especially when integrated with validated metrics and expert feedback. A competency-based framework incorporating performance analytics and learning curve insights is essential for effective training. Future research should prioritize AI-enhanced, personalized training platforms and long-term validation of simulator-acquired skills in clinical practice.