## 2 AI Methods
### Applications of Neural Networks  
Thanks to their non-trivial properties, ANNs have a wide range of applications. Over the years, architectures and variants have been developed that are tailored to working with specific data types and solving specific problems.  
Computer vision (CV) is a foundational application of artificial neural networks (ANNs), for which convolutional neural networks (CNNs) [31] were explicitly designed. The primary difference between ANNs and CNNs is that ANNs are well suited for solving complex problems, whereas CNNs excel in solving computer vision-related problems. A CNN processes data in the form of two- or three-dimensional tensors using the discrete convolution operation, primarily for image data. The primary task of CV is image classification, which is particularly appreciated in clinical applications [32]. Over the years, other CV applications have also been described, such as object detection [33] (Figure 1), image segmentation [34] (Figure 2), and new image generation [35]. Specialised ANN architectures based on CNNs have also been developed for this purpose, such as YOLO [36], SSD [37], UNet [38], and GAN [39], which have seen significant improvements over the years [40; 41].  
Another typical application of ANNs is forecasting sequential data in the form of time series. For this purpose, the recurrent neural network (RNN) was developed [44].  
Figure 1: Example of object detection in a skin lesion photograph. Source: HAM1000 dataset [42; 43].  
Figure 2: Example of object segmentation in a skin lesion photograph. Source: HAM1000 dataset [42; 43].  
Among time series forecasting applications, forecasting stock market prices [45], analysing market trends [46], and predicting sports results [47] are widespread.  
Among the applications of ANNs, it is also possible to mention natural language processing (NLP) [48], which involves analysing text as a sequence of numbers. For this reason, NLP is often performed using RNNs or more complex architectures, such as Transformers [49]. Popular applications within NLP include sentiment analysis [50], named entity extraction in text [51], text summary generation [52], machine translation [53], and chatbots [54]. Several specialised ANN architectures have been developed to implement these applications, such as Seq2 Seq [? ] or BERT [56], which have seen improvements over the years [57; 58; 59].  
In recent years, generative adversarial networks (GANs) have gained importance in dermatology, enabling the synthesis and translation of skin lesion images for domain augmentation and unification purposes (Table 2). In particular, StyleGAN2 allows the generation of high-quality, diverse samples of melanocytic nevi. At the same time, CycleGAN provides a pairless mapping between clinical and dermatoscopic images, which improves the generalisability of classification and segmentation models.  
The use of StyleGAN2 in dermatology is based on the ability to precisely reproduce the morphological features of melanocytic nevi by injecting a layered style vector, which allows the generation of diverse, realistic samples to balance and extend dermatoscopic sets [60]. CycleGAN's ability to facilitate clinical image translation into dermatoscopic styles (and vice versa) enables the removal of domain mismatches between different acquisition modalities, significantly improving the generalisability of segmentation and classification models on unpaired image-mask pairs [61]. Both methods synergistically enrich the preprocessing and augmentation process, providing the networks with both high-quality synthetic examples and uniform post-style translation samples, which translates into better training stability and robustness to clinical variability.