Platform‐Specific Learning Curves in Robotic‐Assisted Total Knee Arthroplasty: A Systematic Review

Robotic-assisted total knee arthroplasty (raTKA) has seen widespread adoption due to its potential to enhance surgical precision and implant alignment. However, learning curves (LCs) for different robotic platforms remain poorly characterized, complicating training and safe implementation. This systematic review quantified the LC across major raTKA systems, focusing on operative time and the number of cases required to achieve procedural proficiency. A systematic search was conducted on December 16, 2024, across MEDLINE, Embase, Scopus, Web of Science, and CENTRAL, following PRISMA 2020 guidelines (PROSPERO: CRD420251026692). Studies reporting original data on LCs in raTKAs were included. Outcomes were operative time, radiographic alignment, complication rates, and patient-reported outcome measures (PROMs). Due to methodological heterogeneity, meta-analysis was not performed; weighted means were calculated where appropriate. Forty studies were included, comprising 10,533 procedures across nine robotic platforms. Operative time, reported in 38 studies, was the primary LC metric. Cases required to reach proficiency ranged from 2 to 73. Stratified analysis showed proficiency after a mean of 18.4 cases for NAVIO (mean time 81.9 min), 29.5 cases for ROSA (85.5 min), and 34.2 cases for MAKO (82.0 min). Radiographic accuracy and complication rates remained stable throughout. PROMs were underreported and inconsistent, limiting conclusions. The LC for raTKA is platform dependent. Inconsistent reporting of radiographic and safety outcomes, especially with ROSA, limited secondary endpoint analysis. These findings highlight the need for standardized LC definitions and robust comparative studies to guide training, accreditation, and safe clinical integration.