## 4 Discussion
In this study, neural networks were introduced as a tool to increase the statistical power of clinical studies without recruiting additional volunteers and avoiding all the drawbacks that follow. Thus, the use of VAE in the analysis of clinical trials can reduce the time needed for completing a trial, reduce costs, and certainly be fully in line with the ethical concerns of reducing unnecessary human exposure in clinical trials. In particular, this study introduced the use of variational autoencoders in the statistical analysis of clinical trials. A literature search revealed that there is a lack of research pertaining to the use of artificial intelligence in clinical data augmentation. It is evident that while there are articles that evaluate data augmentation methods for image data, there is a dearth of literature that comprehensively assesses data augmentation techniques for numerical data in the clinical trials field [17; 18]. A recent study tried to increase the quantity of data derived from a limited sample size [19]. This study aimed to create a simulated population of human coronary arteries for the purpose of conducting in silico clinical trials on stent design. The findings exhibit promise; however, there is a lack of information pertaining to the requisite sample size needed to maintain the statistical properties of the produced data in comparison to the original dataset. One objective of this investigation was to examine precisely that [19]. The recognition of the potential of AI in clinical trials has prompted the examination and discourse of its diverse implementations in the literature [7; 10]. Artificial intelligence possesses the ability to predict the probability of trial or site failure, along with patient outcomes. Also, AI possesses the ability to scrutinize medical records to ascertain suitable groups for clinical trials [10]. The acceleration of trial recruitment can be achieved through the implementation of AI technologies, which can effectively notify medical personnel and patients about available trial opportunities. Moreover, the implementation of AI technology facilitates the streamlined monitoring of clinical trials, while the simplification of entry criteria can potentially improve accessibility for prospective participants. Thus far, data augmentation approaches have been applied in the field of image analysis. Indeed, considerable emphasis has been placed on the utilization of data augmentation techniques in the field of computer vision, particularly with respect to images. Quite recently, [PERSON] presented a comprehensive survey of prior studies that have investigated the field of data augmentation in the context of medical imagery [17]. The objective of these studies was to enhance image data through the implementation of various techniques such as random rotation, noise addition, sharpening, and shifting [20; 21]. Other advanced techniques, including generative adversarial networks, have also been proposed for similar purposes [22; 23]. In the same vein, researchers have attempted to generate authentic fetal ultrasound images in the context of medical imaging [24]. The process involved the extraction of the region of interest from each image using various techniques, followed by the application of a de-noising algorithm and, ultimately, an approximation method. In the present study, as the data consisted of randomly generated numerical values conforming to a normal distribution, the initial two steps were deemed unnecessary. Variational autoencoders have also been used in the augmentation of medical images [25]. Additionally, [PERSON] and colleagues attempted to implement data augmentation techniques for high-dimensional data, such as images [26]. VAEs have already been successfully applied in diverse scientific areas. In a study, a conditional VAE was used for intrusion detection in an internet of things network [27].