Technology-enhanced training in basic robotic surgical skills: a systematic review

Clinical Snapshot

Key Findings

• P Value: Not reported at the systematic review level; individual study p-values not synthesised

• Effect Size: Not quantified — narrative synthesis only; no pooled effect sizes reported. VR demonstrated accelerated learning curves; sensor-based systems improved force application and ergonomics; AR improved instrument velocity; multimodal feedback outperformed conventional feedback; video review improved accuracy but transiently increased task completion time

• Primary Outcome: Technology-enhanced training modalities (VR, AR, sensor-based systems, video review, multimodal feedback) demonstrated improved performance outcomes and accelerated learning curves for basic robotic surgical skills compared to conventional training, predominantly at Kirkpatrick level 2 (learning)

• Nnt Or Sensitivity: Not calculable from available data — no pooled effect estimates, NNT, or diagnostic accuracy metrics reported. Kirkpatrick level 3–4 outcomes (behaviour transfer, patient results) absent from all included studies

• Confidence Interval: Not reported — meta-analysis was not performed due to methodological heterogeneity

Clinical Application

Feasibility varies substantially by technology: VR robotic simulators (e.g., da Vinci Skills Simulator) are increasingly available in high-volume robotic surgery centres but require significant capital investment. Sensor-based force measurement devices and video review systems are more accessible and lower cost. AR systems remain largely experimental. Implementation requires institutional commitment to structured training curricula, dedicated simulation time, and faculty oversight. The absence of standardised competency benchmarks remains a practical barrier to widespread adoption Robotic surgery in Australia is expanding rapidly, with the da Vinci Surgical System used across major metropolitan hospitals in general surgery, urology, and gynaecology. The Royal Australasian College of Surgeons (RACS) and relevant specialty societies (USANZ, AGES, CSSANZ) are developing robotic credentialling frameworks, making this review timely. The TGA regulates robotic surgical systems as Class IIb–III medical devices; training technology platforms used in simulation are generally not subject to TGA regulation unless used for diagnostic purposes. PBS implications are indirect — robotic procedures attract MBS item numbers in limited contexts, and training efficiency improvements could support broader procedural access. The RACGP is not a primary stakeholder, but rural and regional surgical training programmes may benefit from VR-based remote simulation capabilities. Australian surgical training programmes should note the single-centre, predominantly Northern Hemisphere evidence base and exercise caution in direct applicability assumptions. Surgical trainees and credentialling candidates undertaking basic robotic surgical skills training, particularly in general surgery, urology, gynaecology, and colorectal surgery programmes. Most applicable to early-stage robotic training (console skills, instrument handling, dexterity tasks) rather than advanced procedural competency

Abstract

Robotic surgery has seen substantial growth, however training access remains limited by resources and time-intensive credentialling pathways. Technology enhanced learning may address these barriers whilst improving training efficiency and assessment objectivity. We aimed to systematically evaluate technology-enhanced approaches to basic robotic surgical skills training. This systematic review followed PRISMA guidelines with prospective registration (OSF: V93MZ). MEDLINE (via OVID), Embase, and the Cochrane Library (CENTRAL and CDSR) were searched on 23 May 2025 without date restrictions. Technologies included virtual reality, augmented reality, sensor-based systems, video review, and performance feedback. Risk of bias was assessed using the RoB-2 and ROBINS-I tools, and certainty of evidence using GRADE. Narrative synthesis was utilised given methodological heterogeneity, with reported outcomes mapped to the Kirkpatrick model of training evaluation. Seventeen studies involving 484 participants were included (10 randomised trials, 7 prospective cohort studies; 16 single-centre). Technologies comprised virtual reality (7), sensor-based systems (5), performance feedback (3), video review (1), and augmented reality (1); no eligible artificial intelligence studies were identified. Virtual reality, utilised within bespoke robotic simulators, demonstrated accelerated learning curves, although some systems required more repetitions than traditional robotic simulator-based training. The single augmented reality study improved instrument velocity. Sensor-based force measurement devices improved force application and ergonomics. Video review improved accuracy but temporarily increased completion time. Multimodal feedback systems outperformed conventional feedback. Evidence was concentrated at Kirkpatrick level 2 (learning); one study reported only level 1 (reaction) data, with no studies reporting level 3 or 4 outcomes. Risk of bias was low in randomised studies and low-moderate in non-randomised studies. Technology-enhanced basic robotic skills training has evidence supporting improved performance outcomes, accelerated learning curves, and enhanced objective assessment capabilities. Gaps persist for basic skills training in augmented reality, artificial intelligence, and docking and setup. Standardised competency assessment frameworks are required to evaluate these rapidly progressing tools.

References

1. 1.Shakir, T., Lingam, G., Boal, M., Francis, N., & Chand, M. (2026). Technology-enhanced training in basic robotic surgical skills: a systematic review. Journal of Robotic Surgery. Advance online publication. PubMed ID: 42474833