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Background <p>Robotic-assisted surgical training (RAST) has become an increasingly common element of medical education. This systematic review and meta-analysis set out to determine how RAST influences key training outcomes, including technical performance, efficiency, error reduction, cognitive workload, and skill retention in medical trainees.</p> Methods <p>A comprehensive search was conducted through July 2025 to identify randomized controlled trials (RCTs) comparing RAST with conventional training methods. Eligible interventions included robotic simulators, dual-console systems, and remote robotic platforms. Certainty of evidence was graded using the GRADE framework. Where possible, pooled analyses were performed with random-effects models.</p> Results <p>Twenty-three RCTs involving 1,199 participants were included. Technical performance emerged as the most consistently reported outcome. Eleven trials demonstrated clear advantages for RAST, with higher scores on standardized assessments of surgical skills. A pooled random-effects meta-analysis using REML with Hartung–Knapp adjustment confirmed a significant benefit (SMD = 0.71; 95% CI: 0.21–1.22; τ² = 0.04; I² = 20%), with a wide 95% prediction interval (− 0.15 to 1.58) reflecting expected variability across future training settings and representing high-certainty evidence. A sensitivity analysis restricted to resident-level participants demonstrated effects in the same direction, supporting the applicability of these findings beyond novice learners. Improvements were observed across multiple domains, including suturing accuracy, knot tying, and overall procedural coordination. Six studies examined task completion times, and although incomplete variance data limited pooling, most reported faster execution with robotic platforms, pointing to efficiency gains. Five trials investigated error rates, with several showing fewer instrument collisions, dropped objects, and intraoperative mistakes in robotic groups. A meta-analysis of two studies yielded a pooled mean difference of − 7.82 errors (95% CI: −23.07 to 7.44), favoring RAST although not statistically significant. Three studies evaluated cognitive workload, consistently reporting reduced mental and physical strain with RAST. Finally, two studies assessed retention: skills were largely maintained at three months when reinforced, but measurable declines were evident by six months without continued practice.</p> Conclusions <p>RAST offers high-certainty improvements in technical performance and shows encouraging, although more variable, benefits for efficiency, error reduction, workload, and retention. These findings support incorporating robotic platforms into surgical training curricula.</p> Trial registration <p>PROSPERO CRD420251072912.</p>

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Efficacy of robot-assisted surgical training in medical education: a systematic review and meta-analysis of randomized controlled trials

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Carlos M. Ardila,
  • Anny M. Vivares-Builes,
  • Daniel González-Arroyave

摘要

Background

Robotic-assisted surgical training (RAST) has become an increasingly common element of medical education. This systematic review and meta-analysis set out to determine how RAST influences key training outcomes, including technical performance, efficiency, error reduction, cognitive workload, and skill retention in medical trainees.

Methods

A comprehensive search was conducted through July 2025 to identify randomized controlled trials (RCTs) comparing RAST with conventional training methods. Eligible interventions included robotic simulators, dual-console systems, and remote robotic platforms. Certainty of evidence was graded using the GRADE framework. Where possible, pooled analyses were performed with random-effects models.

Results

Twenty-three RCTs involving 1,199 participants were included. Technical performance emerged as the most consistently reported outcome. Eleven trials demonstrated clear advantages for RAST, with higher scores on standardized assessments of surgical skills. A pooled random-effects meta-analysis using REML with Hartung–Knapp adjustment confirmed a significant benefit (SMD = 0.71; 95% CI: 0.21–1.22; τ² = 0.04; I² = 20%), with a wide 95% prediction interval (− 0.15 to 1.58) reflecting expected variability across future training settings and representing high-certainty evidence. A sensitivity analysis restricted to resident-level participants demonstrated effects in the same direction, supporting the applicability of these findings beyond novice learners. Improvements were observed across multiple domains, including suturing accuracy, knot tying, and overall procedural coordination. Six studies examined task completion times, and although incomplete variance data limited pooling, most reported faster execution with robotic platforms, pointing to efficiency gains. Five trials investigated error rates, with several showing fewer instrument collisions, dropped objects, and intraoperative mistakes in robotic groups. A meta-analysis of two studies yielded a pooled mean difference of − 7.82 errors (95% CI: −23.07 to 7.44), favoring RAST although not statistically significant. Three studies evaluated cognitive workload, consistently reporting reduced mental and physical strain with RAST. Finally, two studies assessed retention: skills were largely maintained at three months when reinforced, but measurable declines were evident by six months without continued practice.

Conclusions

RAST offers high-certainty improvements in technical performance and shows encouraging, although more variable, benefits for efficiency, error reduction, workload, and retention. These findings support incorporating robotic platforms into surgical training curricula.

Trial registration

PROSPERO CRD420251072912.