###### Abstract  
Determining the appropriate sample size is crucial in clinical studies due to the potential limitations of small sample sizes in detecting true effects. This work introduces the use of Wasserstein Generative Adversarial Networks (WGANs) to create virtual subjects and reduce the need for recruiting actual human volunteers. The proposed idea suggests that only a small subset (\"sample\") of the true population can be used along with WGANs to create a virtual population (\"generated\" dataset). To demonstrate the suitability of the WGAN-based approach, a new methodological procedure was also required to be established and applied. Monte Carlo simulations of clinical studies were performed to compare the performance of the WGAN-synthesized virtual subjects (i.e., the \"generated\" dataset) against both the entire population (the so-called \"original\" dataset) and a subset of it, the \"sample\". After training and tuning the WGAN, various scenarios were explored, and the comparative performance of the three datasets was evaluated, as well as the similarity in the results against the population data. Across all scenarios tested, integrating WGANs and their corresponding generated populations consistently exhibited superior performance compared with those from samples alone. The generated datasets also exhibited quite similar performance compared with the \"original\" (i.e., population) data. By introducing virtual patients, WGANs effectively augment sample size, reducing the risk of type II errors. The proposed WGAN approach has the potential to decrease costs, time, and ethical concerns associated with human participation in clinical trials. s 23 April 2024 21 May 2024 21 May 2024 24 May 2024 26 May 2024  
population, sometimes extending to future contexts. Therefore, it is crucial for the sample to accurately represent the population, a task facilitated by using appropriate sampling methodologies [3]. The determination of sample size aligns with the hypothesis and study design. The fundamental factors for estimating sample size in clinical trials include type I error (i.e., alpha), desired statistical power, measured endpoint(s), variability of the endpoint(s), limits of acceptance, etc. [4]. The sample size must be appropriate--neither excessively large nor too small. An excessively large sample would unnecessarily require the enrollment of more patients than required for the study objectives, which is unethical. Conversely, a sample size smaller than necessary would lack sufficient statistical power to address the primary research question, potentially leading to statistically nonsignificant results solely due to an inadequate sample size (type II or false negative error) [3; 5; 6; 7; 8; 9; 10]. To overcome the constraints imposed by limited sample sizes and reduce human involvement, computational tools can be utilized to expand the sample size using artificial methods [10]. Another strong tool that has emerged in recent years in this direction is the use of artificial intelligence. The number of AI applications has been increasing exponentially in recent years. In healthcare, there are numerous use cases of AI and machine learning, including drug discovery, medicine, dentistry, anesthesiology, and ophthalmology [11; 12; 13; 14; 15; 16]. One recent application of AI proposed by our research group is data augmentation, which involves virtually increasing a sample by generating new data from existing data [17; 18]. In this context, several other studies have evaluated the effectiveness of diverse augmentation approaches where the Wasserstein Generative Adversarial Networks (WGANs) have exhibited superior performance compared with other methods [19]. Introduced by [PERSON] [20], Generative Adversarial Networks (GANs) are a type of generative model capable of producing novel content based on some training data. GANs find application in various medical fields, including oncology for developing new molecules and enhancing image resolution [21; 22; 23].