Artificial Intelligence in knee arthroplasty

Abstract

Background: Artificial intelligence (AI) encompasses algorithms capable of reasoning and complex decision-making. In knee arthroplasty (KA), the rapid evolution of machine learning, natural language processing, and computer vision has introduced various tools designed to optimize patient care. However, the clinical integration of these technologies requires rigorous validation to distinguish clinically relevant applications from industry-driven trends. Objective: This review aims to provide a comprehensive analysis of AI-based tools utilized in the preoperative, perioperative, and postoperative management of patients undergoing KA, evaluating their clinical relevance and current limitations. Key Points: Preoperative AI applications include predictive modeling for patient selection, immersive virtual reality for surgical education, and automated 3D bone segmentation for preoperative planning and implant sizing. Perioperatively, semi-autonomous robotic-assisted systems utilize machine learning to enhance the accuracy of bone resections, ligament balancing, and component alignment. Augmented and mixed reality platforms offer real-time intraoperative navigation with a smaller physical footprint than traditional robotics. Postoperatively, remote patient monitoring via wearable technology and smartphones allows for continuous data collection and tracking of rehabilitation progress. Despite these advancements, current predictive models for clinical outcomes are limited by potential data biases, ethical concerns regarding data ownership, and the need for larger, representative datasets to replicate clinical acumen. Conclusion: AI-based tools in KA demonstrate significant potential to improve surgical precision, personalize patient pathways, and enhance postoperative monitoring. While these technologies assist in clinical decision-making, further high-level evidence is required to confirm their cost-effectiveness and long-term impact on patient outcomes.

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