Training and credentialing in robotic general surgery

Robotic-assisted surgery is now embedded across many domains of general surgery; however, training and credentialing frameworks have not evolved at the same pace as technological adoption. As a result, significant variability exists in access to training, definitions of competency and proficiency, assessment standards, and institutional credentialing practices. These inconsistencies raise concerns regarding patient safety, equity of access, and workforce preparedness, particularly in the context of expanding robotic platforms and parallel learning curves among trainees and established surgeons.

This narrative review aims to (1) clarify and define key concepts relevant to robotic surgical training, including competency, proficiency, benchmarking, and credentialing; (2) synthesise current barriers to effective robotic training across the surgical career continuum; and (3) propose a structured, standardised, and platform-agnostic credentialing pathway for robotic general surgery. Drawing on published literature and international consensus work, we identify challenges related to system access, lack of standardisation, training capacity, service pressures, and limited use of objective performance metrics and feedback mechanisms.

We propose a competency-based credentialing framework incorporating simulation-based foundational training, modular procedural progression, non-technical skills development, structured mentorship, and objective assessment using validated metrics. The pathway is designed to be adaptable across institutions and applicable to both surgical trainees and consultants, while remaining independent of vendor-specific credentialing models.

Establishing a standardised approach to robotic training and credentialing is essential to ensure safe implementation, support proficiency development, and enable equitable access to robotic surgery. Coordinated action from professional bodies, training institutions, and healthcare systems will be required to deliver a future-ready robotic surgical workforce.