Review Separates Artificial Intelligence From Robotic Execution In Total Knee Arthroplasty
Review Separates Artificial Intelligence From Robotic Execution In Total Knee Arthroplasty
A narrative review published in Arthroplasty analyzes literature from 2015 to 2026 to distinguish between robotic execution and artificial intelligence in total knee arthroplasty. The authors find that while robotic platforms have mature evidence for technical precision, AI models operate primarily in adjacent tasks such as radiographic interpretation, implant sizing prediction, and risk stratification. The review concludes that direct clinical evidence demonstrating incremental patient-level benefit from AI beyond conventional robotic assistance remains limited, noting a disconnect between technical precision and patient-centered outcomes.
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The review distinguishes between robotic execution, which has established evidence for technical precision in bone resection and alignment, and artificial intelligence functions, which are currently limited to adjacent tasks like prediction and monitoring without direct clinical validation for incremental patient benefit.
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tes robotic total knee arthroplasty beyond established mechanical precision, requiring rigorous multicenter trials to verify whether algorithmic tools enhance long-term patient outcomes, implant survivorship, and equitable care. Subject of Research: Evidence distinctions between artificial intelligence and robotic execution in total knee arthroplasty Article Title: Distinguishing artificial intelligence from robotic
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the technological evolution of knee replacement. Conventional manual total knee arthroplasty remains the clinical benchmark, supported by decades of registry data and follow-up for end-stage knee osteoarthritis. Robotic platforms such as Mako, ROSA, and CORI or NAVIO, alongside optical navigation systems, were introduced to improve the reproducibility of bone cuts, component positioning, and limb alignment while redu
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erate accuracy, with receiver operating characteristic area under the curve values ranging between 0.70 and 0.82. However, the review underscores that these studies remain predominantly retrospective, single-institution investigations with sample sizes spanning several hundred to a few thousand patients. External validation within independent cohorts was reported in only a minority of studies, while algorithmic calib
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searching from relevant reviews. The search encompassed peer-reviewed English literature published between January 2015 and January 2025, capturing a decade marked by the clinical expansion of robotic platforms and orthopaedic machine-learning prediction literature, with the final search run on 15 April 2026. Search concepts targeted total knee arthroplasty, robotic-assisted surgery, computer navigation, digital plan
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cing radiographic outliers. Comparative studies demonstrate that these robotic and navigation systems provide mature evidence for improving technical execution accuracy relative to manual instrumentation. However, the authors emphasize that robotic systems execute pre-programmed resection boundaries or haptic guidance without incorporating machine learning. Conflating these domains by attributing the mechanical preci
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Source：Surgical robotics — News discovery · bioengineer.org