## 4 Discussion
Our research suggests using artificial intelligence as a tool for analyzing facial aesthetics, highlighting areas which could be improved, and discusses possible improvements are directions which merit further scientific research. AI's reliability and transparency is something we feel would benefit from further research and further focus should also be on concerning the protection of private data, which is of key importance in a healthcare settings. A possible area for further research is AI systems that use a local database without uploading, \"learning from\" and memorizing data of healthcare importance--so-called federated learning. In such a system, the model (the AI software) could be trained with data, without actually uploading them from a local computer. AI has great potential to help humanity with boring or risky tasks, but also has potentially harmful effects [74; 110]. AI can significantly affect an individual's life when used to discover illness or evaluate risks. Further research should focus on the AI's effect on both individuals and society. Since AI may have negative effects that are difficult to predict and control, researchers should focus on accountability, transparency, good software, and quality sources of information for AI to help AI reach its maximal potential, safety, and benefits [74; 111]. Additionally, it would be interesting to match artificial intelligence both with smartphone applications or daily used computer software in order to test the reliability of AI-based programs in daily clinical practice more easily [112; 113]. Future research could also focus on potential biases in the AI-driven improvements and the psychological effects of AI-enhanced images. Bilateral symmetry, averageness, facial normality, youthfulness, clean skin, sexual dimorphism, and the Golden ratio--these features and parameters greatly influence attractiveness [3; 21; 114; 115; 116]. Other research teams add facial profile and proportions to the attractiveness matrix [117; 118]. Our results show that AI changes facial proportions and features in a favorable way, and thus, we suppose orthodontists could, after much technological developments, modify treatment plans according to the AI's suggestions. After all, one could consider AI and its algorithms as crowdsourcing, taking models from various databases into account. In the past, crowdsourcing has been proven efficient, leading to novel knowledge [119]. Data collection remains to play a pivotal role in further research, and it may be wise to streamline modern methods of information retrieval. However, AI has been proven to be biased before. AI uses huge datasets to train itself, and these datasets typically come from the Internet. As a result, they encode inequalities, stereotypes, and power asymmetries throughout society, as they are commonly trained on white faces [120]. Moreover, a phenomenon termed AI hyperrealism has emerged, which refers to the fact that AI-generated faces are perceived to be more \"human\" than actual human faces, and this points towards the unnaturally high credibility of AI, which is its disadvantage [106]. If the programming or other aspects of deep learning go awry or the AI is exposed to some challenges for the first time, automated treatment planning might produce undesired results. Following such incorrect directions by untrained providers could potentially harm patients and hinder in achieving desired treatment outcomes, not to mention patients' unrealistic expectations from the very beginning, which could also have a detrimental effect on their compliance and willingness to continue the treatment. Being not trained enough is one of the reasons why some authors stress the importance of reviewing the results given by AI to prevent mistakes and damages [121]. For that, however, one needs to be an educated and skilled both clinician and analyst. That is why it seems that, for time being, orthodontists are indispensable and cannot be fully replaced by machines.