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<p>Technology-enabled total joint arthroplasty has bifurcated into two converging paradigms: robotic-assisted arthroplasty (RAA), dominated by haptic-bounded semi-active arms, and AI-augmented arthroplasty (AIAA), propelled by computer vision, machine-learning analytics, and sensor-based implants. While both aim to eliminate alignment outliers and improve patient satisfaction, their comparative advantages, limitations, and synergistic trajectory remain incompletely synthesised. This narrative review evaluated peer-reviewed and grey literature published between 2019 and May 2025, retrieved from seven bibliographic databases, regulatory filings, and conference proceedings. In total, 1,529 records were identified and screened (1,482 from databases and 47 from grey literature). Eighty-three studies meeting predefined inclusion criteria were included in the narrative synthesis. Recent AI-guided vision systems report coronal alignment accuracy approaching that of semi-active robotic platforms, suggesting narrowing technical differentials. AI-guided workflows demonstrated earlier return to functional milestones and comparable patient-reported outcomes to conventional techniques at one year, while robots retained advantages in severe deformities. Smart-implant telemetry coupled with deep-learning alerts has shown preliminary signals of reduced early dislocation rates in hip cohorts, although long-term comparative outcome data across healthcare systems remain scarce. Preliminary cost-utility modelling suggests lower capital barriers for software-based AI augmentation, although long-term comparative durability data remain limited. The next generation of intelligent arthroplasty platforms will likely integrate robotic execution with adaptive AI-driven analytics, forming closed-loop surgical ecosystems. Optimal value requires integrated hybrid platforms governed by transparent interoperability standards, carbon-aware procurement, and equitable access frameworks, underpinned by rigorous multicentre trials and multidisciplinary collaboration.</p>

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Robotic-assisted and AI-augmented arthroplasty: converging technologies in joint replacement

  • 期刊论文

  • 发表日期：

    2026年4月21日

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  • Anil Kumar Kotteda,
  • Utkarsh Kumar Reddy Gopavaram,
  • Sai Surya Dinesh Pydi,
  • Talari Saikumar,
  • Akshay A. Shreegan,
  • Sai Abhiram Reddy Katikareddy

摘要

Technology-enabled total joint arthroplasty has bifurcated into two converging paradigms: robotic-assisted arthroplasty (RAA), dominated by haptic-bounded semi-active arms, and AI-augmented arthroplasty (AIAA), propelled by computer vision, machine-learning analytics, and sensor-based implants. While both aim to eliminate alignment outliers and improve patient satisfaction, their comparative advantages, limitations, and synergistic trajectory remain incompletely synthesised. This narrative review evaluated peer-reviewed and grey literature published between 2019 and May 2025, retrieved from seven bibliographic databases, regulatory filings, and conference proceedings. In total, 1,529 records were identified and screened (1,482 from databases and 47 from grey literature). Eighty-three studies meeting predefined inclusion criteria were included in the narrative synthesis. Recent AI-guided vision systems report coronal alignment accuracy approaching that of semi-active robotic platforms, suggesting narrowing technical differentials. AI-guided workflows demonstrated earlier return to functional milestones and comparable patient-reported outcomes to conventional techniques at one year, while robots retained advantages in severe deformities. Smart-implant telemetry coupled with deep-learning alerts has shown preliminary signals of reduced early dislocation rates in hip cohorts, although long-term comparative outcome data across healthcare systems remain scarce. Preliminary cost-utility modelling suggests lower capital barriers for software-based AI augmentation, although long-term comparative durability data remain limited. The next generation of intelligent arthroplasty platforms will likely integrate robotic execution with adaptive AI-driven analytics, forming closed-loop surgical ecosystems. Optimal value requires integrated hybrid platforms governed by transparent interoperability standards, carbon-aware procurement, and equitable access frameworks, underpinned by rigorous multicentre trials and multidisciplinary collaboration.