The learning curve of robotic esophagectomy: a systematic review with a proposed training and competency assessment...

Robotic-assisted minimally invasive esophagectomy (RAMIE) is a significant evolution in esophageal cancer surgery, providing improved visualization, dexterity and operative precision. However, RAMIE is technically demanding and has a steep learning curve. The goal of this systematic review was to assess the existing evidence on the learning curve of robotic esophagectomy and to develop a structured training and assessment framework for RAMIE.

We performed a systematic literature search in PubMed/MEDLINE, Scopus and the Cochrane Library from inception to June 2026 according to the PRISMA 2020 statement. Studies that assessed the learning curve or implementation of robotic esophagectomy in adult patients were included. The outcomes included proficiency thresholds, operative time, recurrent laryngeal nerve (RLN) injury, complications, lymph node harvest, and factors affecting learning progression. The risk of bias was assessed with the Newcastle-Ottawa Scale (NOS).

We included 25 studies published between 2013 and 2026. Most studies were retrospective observational cohorts from high-volume centers in East Asia, Europe and North America. The most common assessment methodologies of the learning curve were based on cumulative sum (CUSUM) analysis. Typically, initial proficiency was attained after approximately 20-50 cases across studies, but stabilization of complications and optimization of oncologic quality often required higher procedural volumes. Increasing surgeon's experience was associated with improvements in operative time, RLN injury, lymph node harvest, conversion rates, and postoperative complications. Prior experience in minimally invasive esophagectomy, structured proctoring pathways, high institutional volume, and team-based implementation models consistently enabled more rapid proficiency acquisition and safer RAMIE implementation.

The learning curve for robotic esophagectomy is steep, but attainable. Structured training programs, dedicated robotic teams, and standardized implementation pathways may shorten the learning curve and enhance patient safety. Learning curve progression appears to be influenced by factors beyond case volume alone, supporting the development of structured training and assessment frameworks during RAMIE implementation. Based on the available evidence, we propose an evidence-informed expert framework that should be considered hypothesis-generating and requires prospective validation. Prospective multicenter studies and standardized reporting of learning curves are required to optimize training in robotic esophageal surgery and to establish universally accepted thresholds of proficiency.

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD420261417234.