The success of radial head arthroplasty (RHA) relies on the design of the implant and precision of the surgical technique, with preoperative planning potentially playing a crucial role. The accurate establishment of a patient-specific anatomical coordinate system (ACS) is essential for this planning process. This study tested the hypothesis that an innovative automated method would be an accurate, reliable, and efficient framework to determine the ACS of the proximal radius, which would be a step toward improving the precision of RHA planning. We used advanced computational techniques to analyze 50 forearm CT scans, comparing the accuracy, reproducibility, reliability, and efficiency of the automated method with manually derived ACS using expert observers as benchmarks. The results showed that the automated approach was more accurate in identifying anatomical landmarks, with smaller mean distance discrepancies (0.6 mm) than manual observers (1 mm). Its reproducibility was also superior, with narrower reproducibility limits, particularly for ulnar notch landmarks (0.6 to 0.8 mm compared to manual selection 1.2 to 1.4 mm) (p = .01). In addition, the limits of agreement and the mean absolute rotational and translational differences of the axes were narrower for the automated method, which also reduced the construction time to an average of 46 s compared to 150 s manually (p < .001). These findings suggest that the automated method has the potential to enhance the accuracy and efficiency of preoperative and postoperative computer-assisted procedures for RHA. Further research is needed to fully understand the utility of this automated system for enhancing RHA computer-assisted surgical planning.