Robotic-assisted total knee replacement: a narrative review of evolution, clinical impact, and future prospects in AI-driven precision surgery

Abstract

Introduction: Robotic-assisted total knee replacement (RA-TKR) has established a new standard for surgical precision. However, the translation of this mechanical accuracy into consistently superior long-term clinical outcomes remains debated. This has shifted focus toward the integration of artificial intelligence (AI) and machine learning (ML), not as separate tools, but as synergistic partners to robotics. Aim: This narrative review proposes a conceptual framework for an AI-driven robotic ecosystem in total knee arthroplasty. We examine the evidence supporting the independent contributions of robotic precision and AI-based analytics and explore how their integration may support a data-informed, adaptive surgical workflow. Materials and methods: A structured narrative review of peer-reviewed literature on RA-TKR and AI/ML applications in orthopedics was conducted. Evidence was synthesized to support the proposed ecosystem framework. Results: The evidence confirms that robotic platforms consistently improve implant alignment and reduce outliers, though their impact on long-term patient-reported outcomes is less clear. In parallel, AI/ML applications demonstrate significant capabilities across the surgical workflow, including predictive analytics for preoperative planning, real-time intraoperative guidance, and personalized postoperative outcome forecasting. Our synthesis reveals that the synergy of these technologies creates a feedback loop where surgical and outcomes data continuously refine predictive models. Conclusion: The future of knee arthroplasty is likely to depend not solely on enhanced mechanical precision but on the judicious integration of robotics with data-driven intelligence. An AI-driven robotic ecosystem offers a promising conceptual model for advancing personalized, predictive care; however, its clinical value and economic sustainability require validation through robust prospective studies.

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