## 4 Decision Support for Orthodontic Extractions  
In addition to the applications of AI in orthodontic diagnostics presented so far, modern AI algorithms can also be used to support therapeutic decision-making [62; 63; 64; 65; 66; 67]. One example is the decision for or against indicated tooth extractions in orthodontic therapy. With the multitude of clinical, radiographic, and even sociocultural factors that must be considered when deciding on the indication for orthodontic extraction therapy, such decisions remain challenging even for highly experienced orthodontists [68; 69; 70; 71]. It is difficult to make \"an ideal decision\" in the patient's interest [72] as it also depends on the personal training, experience, and philosophy of the practitioner [69; 70; 73; 74; 75; 76; 77; 78]. Therefore,it is not uncommon for experts to arrive at different conclusions, therefore making different decisions, especially in borderline cases [79; 80; 81].  
In recent years, there have been several approaches to automatize and objectify this complex decision-making process through the use of AI [68; 69; 72; 82; 83; 84; 85]. For this purpose, different algorithms have been trained on a large number of patient examples consisting of a selection of clinical factors, radiological findings, model parameters, and the corresponding expert assessment for or against orthodontic extraction therapy. The first studies show promising results with a \"correct\" prediction between 80 and 94% for whether an extraction is necessary [67; 82; 83; 85]. Important orthodontic parameters, such as the extent of crowding, the position of the anterior teeth, overjet, and overbite, as well as lip closure, were identified by the AI algorithms, which significantly influenced the extraction decision [72; 85; 82]. [PERSON] et al., showed that AI algorithms could predict the decision to extract particularly reliably when radiological findings and model parameters were used together as a diagnostic basis [68].  
Furthermore, some studies investigated the extent to which AI algorithms could additionally predict the ideal extraction pattern, for example, a combination of extractions of first and second premolars in the different quadrants. A correct prediction of the extraction pattern in about 84% of all cases was presented [82; 83].  
Despite these very promising results, the aforementioned studies have substantial limitations. Important dental findings, such as the presence of large fillings, endodontic pretreatments, periodontal damage, apical lesions, or missing teeth, were not taken into account, neither in the decision-making process for or against the orthodonically indicated extractions nor in the selection of the extraction pattern [68; 69; 72; 82; 83; 84; 85]. Moreover, the majority of manuscripts published at present share the same limitation that the AI algorithms were set up and trained on examples provided by only a few experts and therefore were trained largely based on the subjective treatment philosophy of these examiners [68; 69; 82; 83; 85]. To avoid this problem, sample data for training the AI would have to be provided by as many experts as possible [72].  
At this point, it is important to consider that--especially in borderline cases--there is often no definite decision for or against orthodontic extraction therapy. The necessity of carefully weighing the advantages and disadvantages of both treatment options against each other continues to persist for every clinician.