## 4 Discussion  
The aim of this study is to propose a novel approach for data augmentation in clinical research by utilizing generative AI algorithms like WGANs. [PERSON] et al. (2018) have highlighted persistent delays in clinical trials, impacting both budgets and research outcomes. Inadequate participant recruitment leads to low-value trials and resource wastage, while the involvement of the private sector in trial financing adds complexity to the landscape. Furthermore, trial duration emerges as a significant cost driver, with each additional month in phase III trials resulting in a median expenditure of $671,000. Consequently, even marginal reductions in trial cycle times could yield substantial cost savings in overall clinical development budgets ([PERSON] et al., 2018). Figure 9: Percent similarity in the performance among the “original”, “sample”, and “generated” datasets when the size of the “sample” dataset is either equal to 50 (**left** panel) or 100 (**right** panel). The sizes of the “Generated” samples are equal to 1000 (**A,D**), 500 (**B,E**), and 100 (**C,F**). In all cases, the between-subject variability is 10%. Under each scenario, a number of 5000 Monte Carlo clinical trials are simulated. The exponential emergence of AI methodologies during the last years started to grow within the pharmaceutical and healthcare sectors. The utilization of AI-driven algorithms enables healthcare professionals to analyze extensive patient datasets, uncovering patterns that may elude human clinicians initially. This capability facilitates early disease detection, prompt medical interventions, and improved patient prognoses. Additionally, AI has the potential to predict treatments most likely to yield positive outcomes for individual patients, enabling personalized medicine tailored to unique patient characteristics. By optimizing administrative processes and reducing redundant tests, AI also holds promise for reducing healthcare expenses, both in terms of finances and time [11]. In this context, this study introduces a novel method aimed at decreasing sample size in clinical trials by using WGANs. The proposed idea is simple: to draw conclusions from a small sample in a clinical trial yet achieve the same statistical power as that of an entire population in a clinical trial. As achieving this using traditional statistical methods is impossible, we used a generative AI algorithm (WGANs) to tackle this challenge. The newly proposed method involves training WGANs on a limited sample and utilizing it to generate \"virtual patients\". To evaluate the efficacy of the current method, we conducted Monte Carlo simulations of clinical studies to compare the performance of the WGAN-generated virtual population (referred to as the \"generated\" dataset) with both the original population and a small sample. The proposed approach suggests that by combining a small sample from the population with a generative algorithm like WGANs, it is possible to create a virtual population. This can enhance statistical power without raising type I error rates. To accomplish this task, Monte Carlo simulations were applied to generate a virtual population of 10,000 patients, representing the entire population, referred to as the \"original\" dataset. To create the \"sample\", we mimic the process used in real-world scenarios by randomly sampling from the \"original\" dataset. In this study, two small proportions of the \"original\" dataset were utilized (0.5% and 1.0%) corresponding to 50 and 100 subjects in the \"sample\", respectively. WGANs were then used to exploit the information contained in the \"sample\" and to synthesize the \"generated\" dataset (Figure 2). Initially, the size of the \"generated\" data was set as equal to the population size, i.e., 10,000 subjects. Subsequently, smaller sizes of the \"generated\" dataset were used in the analysis, such as 1000, 500, and 100. Pairwise comparisons were then made among the \"original\", \"sample\", and \"generated\" datasets to assess their performance.