## 4 Discussion
Ethical considerations for clinical studies should involve obtaining consent, where applicable, for the use of clinical data in generating synthetic datasets from patients or their representatives. Therefore, appropriate measures should be taken to identify and reduce any biases in the synthetic data generation process to ensure fairness and representativeness of the synthetic datasets. By following these steps, it is anticipated that the synthetic data generated by a generative AI algorithm are both scientifically valid and ethically sound for use in clinical research [50]. The study has some limitations that need to be mentioned. Firstly, the time duration of each scenario to complete all iterations poses a limitation, potentially affecting the robustness of the results. While the precise timeframes and resource allocations may vary depending on the specific experimental conditions, we can provide an approximate range based on our experiments. Typically, model training ranged from 20 to 30 min, with larger datasets and more complex architectures requiring longer training times than the smaller ones. Furthermore, we recognize the importance of optimizing computational efficiency and scalability in future iterations of our methodology. However, in real practice, simulations will not be required; WGANs would be directly applied to the actual \"sample\" human data. In our study, model training was conducted using relatively modest computational resources. Specifically, we utilized two computers equipped with the Windows 11 operating system, each powered by an Intel(R) Core(TM) i5-9400 CPU at 2.90 GHz and equipped with 8 GB of installed RAM. Although these systems are not as powerful as dedicated high-performance computing clusters, they still provided sufficient computational capability to successfully train our model. The Monte Carlo simulations conducted in this study were solely performed to evaluate the applicability of the WGAN approach compared with the traditional \"sample\"-based procedure. Another limitation is the absence of real clinical data in this study; incorporating real-world clinical data will enhance the accuracy and reliability of WGANs, enabling better predictions and informed decision-making in clinical studies. It is important to clarify that our initial use of virtual generated data served as a foundational step in exploring the feasibility and potential benefits of data augmentation techniques, such as the implementation of the WGAN, within the context of clinical studies. As such, our focus is primarily centered on evaluating the efficacy and performance of the WGAN algorithm in generating synthetic data that closely resemble real-world clinical datasets. Training the algorithm with real-world problems would be particularly beneficial in addressing these limitations and improving the utility of WGANs in clinical research. Another issue that should be mentioned relies on the fact that only the simple \(t\)-test criterion was used. While the primary focus was to evaluate the efficacy of the proposed algorithm in generating synthetic patient data across various scenarios, there is potential for further investigation. For instance, including an additional random error following a normal distribution as a reference point for comparison, and comparing WGANs with other methodologies or algorithms, would enrich the analysis. The WGAN data augmentation approach should be further explored in various types of clinical trials, including non-inferiority, superiority, and bioequivalence studies, as well as implementing real-life data from clinical trials to assess the feasibility of the algorithm. Overall, the integration of synthetic data into various research fields is already underway. To our knowledge, there has been no prior exploration of implementing the WGAN algorithm to generate \"virtual patients\" aimed at reducing the time and expenses associated with clinical studies and minimizing human exposure.