Artificial intelligence in arthroplasty: Past achievements, present applications, and future directions

Abstract

Arthroplasty, total hip and knee, has been one of the most effective treatments in orthopedic surgery. With elevated surgical volumes and enhanced demands for precision, economy, and individualized care, interest in the use of artificial intelligence (AI) throughout the arthroplasty continuum is increasing. Initial advancements in computer-aided and robotic technologies laid the groundwork for AI integration, while advancements in machine learning, deep learning, computer vision (CV), and natural language processing are now broadening its applications. The present evidence points toward the application of AI across various fields such as preoperative risk assessment, imaging interpretation, intraoperative robotic assistance, CV-based navigation, and postoperative tracking via wearable devices and predictive models. In addition to direct surgical assistance, AI also helps make operations more efficient in workflow optimization and resource management. Directions of future development include personalized implant design, the creation of semiautonomous and autonomous robotic systems, federated learning models that improve collaboration while maintaining data privacy, and the integration of explainable AI to enhance trust and acceptance. With these encouraging developments, there are significant challenges still to be overcome. Challenges such as variable data quality, bias in algorithms, limited interpretability, insufficient large-scale clinical validation, high cost of implementation, and regulatory risk could impede broad clinical translation. To achieve the maximum potential of AI in arthroplasty, robust validation platforms, ethical and legal protections, fair access plans, and interdisciplinarity among surgeons, engineers, and policymakers will be critical. With thoughtful incorporation, AI can be a pillar of precision, personalization, and value-based care in arthroplasty.

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