## 2 Cephalometric X-ray Analysis  
Cephalometric X-ray analysis, which was introduced by [PERSON] in 1931, is still a cornerstone of orthodontic treatment planning [9]. The first step in the analysis of cephalometric images is identifying landmarks. On the basis of these landmarks, geometric evaluations in the form of angles, distances, and ratios can be performed, enabling a sagittal and vertical analysis of the facial skull [10]. Prior to the use of AI, geometric constructions and measurements were merely facilitated by software solutions, but the identification of the landmarks themselves remained a manual task for the practitioner [11].  
In recent years, various researchers have been able to automatize this time-consuming and error-prone process by using AI-algorithms [2; 10; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. The majority of studies investigating the use of AI for automated cephalometric X-ray analysis evaluate the accuracy of their AI based on the metric deviation between the landmarks set by the AI and the human gold standard. In this context, [PERSON] et al., performed a meta-analysis in which the accuracy of the automated landmark detection of different researchers was analyzed. The authors demonstrated that the majority of the included studies were able to identify landmarks within a metrical tolerance limit of 2 mm [7]. This 2 mm tolerance is generally accepted as sufficiently accurate for clinical purposes in this regard [10; 11; 14; 17; 18; 19; 20; 21; 27; 28]. However, not only the metric deviation but also the direction of this deviation are of decisive importance to determine the actual clinically relevant accuracy of the orthodontic parameters that are measured on the basis of these landmarks. For example, in the case of angular measurements, inaccuracies of the landmarks along the angles' legs do not lead to any changes in the measured parameter [26].  
To circumvent this shortcoming, another approach to assess the accuracy of automatic cephalometric analyses is to evaluate them based on the orthodontic parameters themselves [29]. However, hardly any study in the literature evaluates the quality of AI assessments on this basis. [PERSON] et al., (2020) analyzed the accuracy of their AI for automated cephalometric analysis based on commonly used orthodontic parameters [26]. The authors were able to show that, out of twelve different orthodontic parameters (including skeletal sagittal, skeletal vertical, and dental parameters), only one parameter was found to be significantly different compared to the human gold standard. The average deviations between the evaluations of the AI and the human gold standard were well below one degree for all parameters. It can therefore be assumed that the differences between the predictions of the AI and the human gold standard are clinically irrelevant or of marginal importance at worst.  
Despite all these promising approaches, [PERSON] et al., pointed out that there was an increased risk of bias in the majority of studies investigating the use of AI for the automated analysis of cephalometric images. This fact should be viewed critically as some commercial providers already offer such software solutions for which the underlying scientific data for the AI are inadequately communicated, unclear, or lacking [7]. To date, only a few studies have examined the quality of automated cephalometric analyses from such commercial providers [30; 31; 32; 33; 34]. The results of these studies demonstrated that there are major differences concerning the assessment qualities of those different providers, and most authors, therefore, concluded that fully automated cephalometric analyses should only be used with human supervision by experienced clinicians [30; 31; 35].