Evaluation of the learning curve in robot-assisted knee arthroplasty: A Systematic review

Robot-assisted (RA) surgery has transformed total knee arthroplasties (TKA) and unicompartmental knee arthroplasties (UKA) by significantly enhancing the accuracy of prosthetic implantation and reducing complications. Different robotic systems offer unique approaches to assist surgeons during procedures.

This systematic review adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and evaluated the learning curve associated with RA technologies, focusing on their surgical efficacy. A comprehensive literature search identified and analysed studies published since 2010, ultimately selecting 28 relevant articles that addressed robotic techniques in knee arthroplasty.

The findings indicate that surgeons generally require an average of 21 procedures to achieve proficiency with robotic techniques. However, the learning curve varies among robotic systems: MAKO requires 15-25 cases, ROSA 20-30 cases and NAVIO 18-28 cases for surgeons to reach proficiency. The MAKO system emerged as the most frequently used (33.3% of studies), followed by ROSA (23.3%) and NAVIO (16.7%). Evidence suggests that the adoption of these robotic systems is associated with reduced operative times and lower rates of postoperative complications, thereby improving overall surgical outcomes.

RA arthroplasties present significant advancements in surgical precision and patient outcomes. With targeted investments in training and technology, the adoption of robotic techniques could further increase, ultimately enhancing the quality of orthopaedic care and patient recovery. This review highlights the importance of addressing training needs and resource allocation to fully realise the potential of robotic surgery in knee arthroplasty.

Not applicable, systematic review.