Assessing Effectiveness and Skill Transferability in Multi-Platform Simulated Training for Robotic Surgical Skills: A Systematic Review

Introduction With more robotic platforms approved for clinical use, interest in assessing skill transferability has increased. Skill transfer has yet to be studied amongst trainees in early stages of robotic training. This systematic review evaluated skill transferability of robotic platforms amongst trainees in simulated settings. Methods The systematic review was conducted according to PRISMA guidelines. Five databases were searched from inception until 20 January 2025. Inclusion criteria had studies using simulation-based modalities, more than one robotic platform, and objective metrics or global rating scales for skills assessment. Results Search identified 609 unique studies. Five studies were included. Platforms analysed were Da Vinci X, Si and single-port system, Hugo RAS and CMR Versius. No performance differences seen across platforms amongst novices and experts. Groups with some robotic experience exhibited variable degrees of skill transfer. Prior robotic surgical experience was the main driver for better baseline performance and skills progression. Conclusion Skill transferability was demonstrated in cross-platform simulated training amongst experts and novices, indicating that core robotic surgical skills can be applied across systems. Less evidence of skill transfer in intermediate-level participants suggests that those still consolidating their technical proficiency may be more sensitive to platform-specific differences. Console design variations appeared to influence transferability. Incorporating cross-platform training could enhance trainees’ understanding of the fundamental principles of robotic surgery, equipping them with more adaptable skillsets suited to various platforms. This has important implications for surgical training, as during the trainees’ learning phase, they should be aware of the potential decline in technical performance when transitioning between platforms.