## 4 Discussion
However, the constant nature of traditional standard measurements may continue to offer a fix reference point, and thus, continue to provide a valuable source for consideration. The traditional datasets may also be more transparent than the decision making of AI [103]. Using AI-driven diagnosis and aesthetic planning helps to add a more distanced and more objective input; however, the final decisions should be made by the treating doctor, who should be cautious regarding the treatment plan limitations. Our study has some limitations that need to be addressed as well. The participants were only asked to identify themselves with an age group. This was suggested by a psychologist because asking about one's age might make feel some respondents uncomfortable, and have an effect on their scoring. However, having no exact data about age or profession of the respondents makes more complex analyses impossible. It is well known that one's profession and experience influence one's judgement. For example, orthodontists and laypersons give different importance to various facial features when it comes to a facial attractiveness assessment [25]. Another limitation was the total number of respondents. Out of 1800, only 159 and 282 respondents in the first and second round, respectively, submitted the completed forms. Given the limited number of respondents, it would have been very difficult to make more complex analyses that are proper and relevant, as the more categories of assessors there are, the higher the overall number of respondents is desired. Despite splitting the original form into two forms, it is possible that many respondents considered the form dull and repetitive, and gave up on it halfway through. A possible solution would be to fully explain the aim of the study to the participants to make them feel more involved and responsible for this research; however, this could also impact their judgement due to biases of various types [104; 105]. Our research studied changes of AI-generated faces, not actual human faces. Naturally, the changes of human faces could be quite different from the changes of AI-generated faces, based on the mere fact that they just do not look the same. However realistic the generated faces are, they are different from real-world patients. As described in the section Materials and Methods, we did not ask for any faces with orthodontic anomalies to be generated. In our experience, however, patients with perfect teeth rarely come for diagnosis and treatment. Using AI-generated faces, thus, poses a possible limitation to our study, as the faces did not show any obvious orthodontic anomalies. On the contrary, all the original pictures were quite average faces [106]. Our choice was based on our aims of having a preferably neutral starting point, and on increased protection of personal data. When we upload photos to most current AI systems, we are also letting the AI learn from the uploaded data and our interactions with the AI [74]. Before our research, we did not know the clear benefits of AI facial enhancement of photos. Using actual photos and asking for the patients' consent in this regard was not yet underpinned scientifically to a high enough level to support such actions. Therefore, we took an inspiration from the pharmaceutical industry, in which a computer-aided drug design is streamlined and successfully integrated, using computers and programs first and only later proceeding to other stages of drug discovery [107; 108; 109]. We modified AI-generated faces with AI algorithms to obtain results that could be potentially used as a base for our future study with actual patients. This research was a pilot study, so a rather simple study design was chosen. In more complex future research on this topic, all mentioned limitations should be prevented. Judging by the increase in the number of orthodontics and AI-themed articles, the role of AI is sure to increase with time [29].