Learning Curve in Robotic Colorectal Surgery.

Background and Objectives: Robotic platforms are increasingly employed in colorectal surgery for their technical and ergonomic benefits. However, surgeons face a significant learning curve, and there is no standardized definition or threshold to proficiency. This systematic review aimed to evaluate published evidence on learning curves in robotic colorectal surgery, focusing on proficiency thresholds, analytic methodologies, and the effect of experience on clinical and oncological outcomes. Methods: A systematic literature search of PubMed was performed through April 7, 2025. Studies reporting learning curve data for robotic colorectal procedures were included. Screening and selection were conducted using Rayyan. Extracted data included operative time, case numbers to proficiency, conversion and complication rates, and oncological metrics. Study quality was assessed using the Newcastle-Ottawa Scale. A narrative synthesis was undertaken due to heterogeneity in study design and outcomes. Results: Nineteen studies met inclusion criteria. The number of cases required to reach proficiency ranged from 15-55, with operative time being the most analyzed parameter. CUSUM and RA-CUSUM were the predominant analytic methods. Improved outcomes such as reduced complications, lower conversion rates, and enhanced oncological quality were generally observed in the post-proficiency phase. Variability in learning curve definitions and analytic approaches was significant across studies. Conclusion: Robotic colorectal surgery involves a measurable learning curve that impacts both technical and patient-centered outcomes. While most studies demonstrate improved metrics with experience, the lack of standardized methodology limits cross-study comparisons. Structured training pathways and consensus on learning curve analysis are needed to support safe adoption and credentialing in robotic colorectal surgery.