Robot-assisted total knee arthroplasty: adverse events related to the robotic system

Robot-assisted total knee arthroplasty (RA-TKA) aims to enhance surgical precision and reproducibility. However, technical malfunctions occasionally occur. This study analyses single-centre RA-TKA malfunction cases to explore causative factors and reflect on the relationship between surgeons and robotic assistance. A retrospective analysis was conducted on 188 consecutive RA-TKA procedures performed between January 2020 and June 2025 by the same senior arthroplasty consultant using five distinct robotic systems (Brainlab template-guided, HURWA automated alignment, Smith & Nephew CORI milling-drilling, BoneSage automated alignment, and Jianjia template-guided). Cases meeting predefined ‘malfunction’ criteria. Malfunction defined as: inability to complete critical steps according to the robot’s predetermined plan due to any cause, necessitating intraoperative abandonment of robotic assistance or major plan adjustments; or direct robot-related factors causing poor postoperative alignment (mechanical axis deviation > 3°) or abnormal soft tissue balance requiring early intervention. Systematic classification and statistical analysis of malfunction causes. Seventeen malfunction cases were categorised into three primary groups: (1) Robot malfunction (3 cases, 17.6%): intraoperative robotic system failure; (2) Technical malfunction (7 cases, 41.2%): loosening of positioning/reference frame (5 cases), misplacement of calibrator in osteotomy zone (2 cases); (3) Workflow and learning curve issues (2 cases, 11.8%): significant time prolongation and subsequent abandonment due to unfamiliarity with specific new system procedures (2 cases); (4) Conflict between surgeon decision-making and robotic planning (5 cases, 29.4%): surgeons performed unplanned mobilisation or osteotomy adjustments based on experience, ultimately resulting in failure to achieve anticipated balance/alignment. The success of RA-TKA relies on a reliable platform, standardised procedures, and human-machine collaboration. Clinical observations from 188 cases revealed: (1) 17 cases (9.0%) experienced robot-related malfunctions; (2) Different robotic systems exhibited distinct failure modes; (3) Malfunction rates were higher during the early learning curve phase; (4) All malfunctions were resolved through surgeon intervention without causing surgical failure. This indicates that technical malfunctions can be progressively mitigated through system optimisation and training, while intraoperative human-machine coordination and stepwise verification remain critical for surgical safety.