## 1 Introduction
With the advance of AI into the realm of picture editing, new possibilities present themselves in treatment planning. There have been plenty of applications on the market. Even decades ago, users could add various filters, frames, drawings, texts, or props to make their pictures more appealing. Even more so, picture enhancement technologies, such as directional wavelet transform, histogram equalization, algebraic reconstruction mode, cellular neural networks, partial differential equation, adaptive interpolation method, contrast stretching, alpha rooting, range compression, multi-frame super resolution, and adaptive iterative filtering, have been closely studied for over a decade [66]. Nowadays, AI-powered applications go even further. They combine image processing and machine learning to generally improve the appearance of facial features. The whole process comprises several steps [67]:  
1. Face detection--faces are detected and located using specific face-detecting algorithms that seek and identify facial landmarks and boundaries. 2. Facial feature analysis--facial features (eyes, mouth, skin, nose, ears, etc.) are analyzed, for which the facial geometry needs to be understood. 3. Image processing--certain aspects of the face are enhanced, using various techniques (brightness, contrast, color hue changes, etc.). Deep learning models--models trained on huge datasets of facial images are employed (namely convolutional neural networks) to grasp representations and patterns of facial features. * Feature enhancement--based on learned patterns, specific facial features are enhanced (e.g., nose size, wrinkle reduction, and lip enlargement). * Generative adversarial networks--in some cases, a generator and a discriminator interact in such a way that a generated image is evaluated regarding its authenticity, which yields even more realistic results. * Customization and user preferences--based on the application, the type as well as the extent of desired enhancement is adjusted. * Real-time processing--in case of video processing, multiple processes are employed at the same time. * Quality assessment--in the end, the application itself evaluates the outcomes so that the modifications lead to a visually pleasing and natural-looking outcome. Based on an Internet search (carried out on 6 March 2024), some of the most popular AI-powered face-enhancing applications are: Remini (AI Creativity S.r.l., Milan, Italy), Clipdrop (INIT ML, Montreuil, France), Lensa (Prisma Labs, Inc., Sunnyvale, CA, USA), PhotoApp (ScaleUp, Urla, Turkey), VanceAI (VanceAI Technology, Limited, Hong Kong, China), FaceApp (FaceApp Technology Limited, Limassol, Cyprus), Let's Enhance (Let's Enhance, Inc., San Francisco, CA, USA), Voila AI Artist (Wemagine.AI LLP, Richmond, BC, Canada), Reflect (BrainFeverMedia LLC, West Chester, OH, United States), Fotor (Chengdu Hengtu Technology Co., Ltd, Chengdu, China), Topaz Photo AI (Topaz Labs LLC, Dallas, TX, USA), Adobe Photoshop (Adobe Inc., San Jose, CA, USA), PhotoWorks (AMS Software, Wake Forest, NC, USA), BeFunky (Befunky Inc., Portland, OR, USA), PicMonkey Retouching Tools (Shutterstock Inc., New York, NY, USA), Peachy--Face & Body Editor (Shatman Pet. Ltd., Singapore) [68; 69]. Based on our own research of the listed applications, FaceApp was chosen as the most user-friendly, popular, easy-to-use, and potent application that yields enhanced, yet realistic pictures. The application, released in 2017, uses deep learning--convolutional neural networks specifically--to process images [70]. FaceApp uses various algorithms that use complex data to extract statistical distribution of patterns, with the aim to predict an outcome for an input without being explicitly programmed to do so [71; 72]. Thus, FaceApp is much more than a compilation of various filters. Each picture is translated into a multidimensional vector, which may be further adapted and redistributed throughout the neural network [73].