In recent years, medical technology companies have increasingly been integrating time-of-flight cameras into their X-ray devices to support optimal collimation and the patient positioning process. However, for many hospitals, it is not financially viable to acquire a new X-ray device with such a camera for those features. In order to still have support for e.g. patient positioning, without having to buy a new X-ray device and be dependent on proprietary algorithms, it is possible to only acquire a time-of-flight camera, attach it to the X-ray device, and use custom algorithms. In this work, we evaluated the ideal camera position for AI-supported patient pose assessment based on depth images for such a setup and assessed the usefulness of a setup with multiple cameras. For this, we generated a total of 461,550 synthetic depth images from CT scans from 50 different camera positions and synthetic radiographs in order to investigate in 1,500 experiments how accurately patients’ poses can be assessed with different camera positions. We found that a camera position perpendicular to the target anatomy being radiographed is particularly well suited, and that adding a second camera does not significantly improve performance.