Robotic assistance in total knee arthroplasty

Introduction: Total knee arthroplasty is acknowledged as a gold standard for treating degenerative knee joint diseases, and optimal implant positioning is crucial for successful outcomes. Robotic-assisted TKA emerges as a promising solution for enhancing precision in implant positioning, despite potential drawbacks such as increased surgical duration and associated costs. Various robotic systems, including open, closed, passive, semi-active, and active types, are available on the market, such as ROBODOC®, MAKO®, ROSA®, and NAVIO® each one with certain features. Objectives: The primary objectives of this study are to comprehensively analyze the learning curve, shortterm and long-term clinical outcomes, and prospects associated with robotic assistance in TKA. Results: The learning curve associated with robotic TKA shows the gradual reduction in operation time as surgeons gain experience. Studies underline the importance of surgeon familiarity and experience in optimizing the benefits of robotic assistance. Long-term outcomes obtained through follow-up studies, indicate improved precision in maintaining mechanical axis and alignment of components. Comparative studies between manual and robotic-assisted TKA reveal enhanced compartment balancing and improved patient satisfaction with the latter. The pros of robotic assistance are increased precision, reduced complications, and improved patient satisfaction. Cons include higher maintenance costs and longer operation times during the learning period. Conclusions: Robotic assistance in TKA offers substantial benefits in terms of implant positioning accuracy and patient outcomes. The learning curve is seen as a temporary challenge that diminishes with surgeon experience. Despite concerns about increased costs and potential complications, the long-term advantages may outweigh these issues. Continued research and evaluation are required to refine techniques, enhance efficiency, and make these advancements accessible to a broader population.