Revolutionizing Total Knee Arthroplasty: The Integration and Impact of Artificial Intelligence across the Care Continuum

Abstract

Background: Total Knee Arthroplasty (TKA) is the gold standard for managing end-stage knee osteoarthritis, a condition affecting a significant portion of the aging population. Despite its success, optimizing outcomes in TKA remains challenging due to factors such as implant malalignment and patient-specific anatomical differences. Purpose: This review explores the transformative potential of Artificial Intelligence (AI) in addressing existing challenges in TKA, from preoperative planning to postoperative care. Main body: AI applications in TKA span several areas, including preoperative imaging analysis, patient risk stratification, and personalized surgical planning. Machine learning algorithms can automatically segment knee structures with high precision, enhancing preoperative planning and implant selection. AI-driven imaging analysis can predict surgical outcomes, assisting surgeons in making informed decisions. In patient risk stratification, AI analyzes preoperative data to predict patient-specific risks, enabling tailored interventions. Intraoperatively, AI enhances robotic-assisted TKA systems and navigation technologies, improving surgical precision and allowing real-time adjustments based on patient-specific data. Conclusion: The integration of AI into TKA workflows promises to revolutionize the field by enhancing decision-making, improving surgical precision, and personalizing patient care. As AI continues to mature, it has the potential to optimize outcomes while reducing complications and healthcare costs in TKA.

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