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Abstract Robot-assisted surgeries (RAS) have an extremely steep learning curve. Because of this, surgeons have created many methods to practice RAS outside the operating room. These training models usually include animal or plastic models; however, extended reality simulators have recently been introduced into surgical training programs. This systematic review and meta-analysis was conducted to determine if extended reality simulators can improve the performance of robotic novices and how their performance compares to the conventional training of surgeons on surgical robots. Using the PRISMA 2020 guidelines, a systematic review was performed searching PubMed, Embase, Web of Science, and Cochrane library for studies that compared the performance of robotic novices that received no additional training, trained with extended reality, or trained with inanimate physical simulators (conventional additional training). Articles that gauged performance using GEARS or time to complete measurements were included, while articles that did not make this comparison were excluded. A meta-analysis was performed on the 15 studies found using SPSS to compare the performance outcomes of the novices after training. Robotic novices trained with extended reality simulators showed a statistically significant improvement in time to complete (Cohen’s d = −0.95,p = 0.02) compared to those with no additional training. Extended reality training also showed no statistically significant difference in performance in time to complete (Cohen’s d = 0.65,p = 0.14) or GEARS scores (Cohen’s d = −0.093, p = 0.34) compared to robotic novices trained with conventional models. This meta-analysis seeks to determine if extended reality simulators translate complex skills to surgeons in a low-cost and low-risk environment. 

Robot-assisted femur repair has been of increased interest in recent literature due to the success of robot-assisted surgeries and current reoperation rates for femur fracture surgeries. The current limitation of robot-assisted femur fracture surgery is the lack of large force generation and sufficient workspace size in traditional mechanisms. To address these challenges, our group has created a 3-RRPS parallel mechanism, Robossis, which maintains the strength of parallel mechanisms while improving the translational and rotational workspace volume. In this paper, an optimal design methodology of parallel mechanisms for application to robot-assisted femur fracture surgery using a single-objective genetic algorithm is proposed. The genetic algorithm will use a single-objective function to evaluate the various configurations based on the clinical and mechanical design criteria for femur fracture surgery as well as the global conditioning index. The objective function is composed of the desired translational and rotational workspaces based on the design criteria, dynamic load-carrying capacity, and the homogeneous Jacobian global conditioning index. Lastly, experimental results of Robossis were obtained to validate the kinematic solution and the mechanism itself; Robossis had an average error of 0.31 mm during experimental force testing.