AI-Augmented Robotic Surgery in Orthopedics: A Comprehensive Literature Review on Precision, Efficiency, and Long-Term Outcomes

Abstract

Robotic assistance has played an important role in increasing the accuracy and consistency of orthopedic surgeries during the last 20 years. Introduction of AI into the process has allowed for an increase in the accuracy of surgeries, giving surgeons decision support tools, adaptive learning, predictive algorithms, and individualized treatment options. This literature review describes innovations in the sphere of AI-integrated robotic orthopedic surgery in the period between 2018 and 2025. It focuses specifically on TKA, THA, and innovative developments in spine and shoulder surgeries. Different types of AI approaches, such as CNN for segmentation and computer vision, reinforcement learning for trajectory planning, LSTM and Transformer networks for prediction, and CDS systems that utilize Explainable AI, are examined. Some examples of improvements in terms of precision in implant placement (98.7% precision), reduction of surgery time (15-28% reduction), blood loss (25-35% reduction), and reduced rate of revision after two years and five years follow-up are presented. However, there are still certain problems that have not been solved, namely, the high initial expenses for acquiring the technology, the learning curve, the problem of information security, as well as the lack of generalizability to other patient groups.