## 4 Discussion
Also, similarity with the \"original\" dataset (i.e., the population) was estimated in order to express the concordance in the statistical results between the WGAN-\"generated\" dataset and the \"sample\". The performance of the WGANs towards the scope of the study was assessed under several scenarios like different between-subject variability, difference between the two treatments (expressed as Test/Reference ratios), \"sample\" size, \"generated\" size, etc. In summary, the application of WGANs demonstrated favorable performance. Across all scenarios examined, the AI-generated dataset resulted in better results compared with the \"sample\" and, in most cases, the performance of WGANs was found to be significantly superior to that of the traditional sample. Starting with the 0.5% sampling (equivalent to 50 patients from the population), the \"sample\" fails to capture the population traits (Figure 4). Conversely, the \"generated\" population, shown in Figure 5, consistently mirrors the population characteristics, indicating successful imitation by WGANs. These findings highlight the desired ability of the AI-synthesized data to mimic the behavior of the actual population. The similarity between the \"generated\" and \"original\" populations consistently surpassed that of the \"original\"-\"sample\" pair across scenarios, except in one scenario (T/R = 1.15, CV = 10%). It also highlights the potential risk of false negative errors (i.e., type II error) within the traditional way we perform the clinical trials, a risk which is eliminated by virtually increasing the sample size with WGANs. Conversely, as the T/R ratio increases, success rates decrease for the sample, while they rise with higher variabilities (i.e., CV). Also, with the 1.0% sampling, representing 100 patients from the initial 10,000, across 16 scenarios, the similarity percentages between the \"original\" and the \"generated\" populations were higher in nine scenarios, with seven scenarios showing equal similarities. Notably, as the T/R ratio increased, similarity decreased with higher CVs. WGANs generally handled high variability better than the \"sample\", except when the T/R was 1.0, where both showed similar results (Figures 7 and A1). Also, in all scenarios explored, the \"generated\" population consistently showed higher or equal success rates compared with the \"original\" data (i.e., the population).In the above-mentioned scenarios, the size of the \"generated\" population was matched to that of the \"original\" population, comprising 10,000 subjects, and the observed performance of the WGAN procedure was deemed rather satisfactory. To obtain deeper insights into the WGAN performance, smaller sizes were also examined for the \"generated\" datasets (Figure 9). Again, in these cases, the similarity percentages for the \"generated\" sizes of 1000, 500, and 100 were similar across both sampling percentages. Also, the success rates were similar across all \"generated\" sizes, T/R ratios, and samplings, with generated population success matching that of the population. To this point, it is important to underline some key points about the proposed method. Initially, using synthetic data in clinical trials aims only to improve the statistical power of the study. This is achieved by utilizing WGANs to create virtual subjects based on the limited sample size. This work suggests using WGANs not to replace human volunteers entirely but rather partially. The aim is to reduce human involvement in clinical trials, thus boosting statistical power without needing more participants, thus avoiding ethical concerns, costs, and time limitations associated with recruiting additional human participants. However, it is essential to have a sufficient and representative sample of human volunteers to accurately generate virtual subjects. And it is also worth mentioning that data synthesized by WGANs are not intended to be used for safety evaluation. Another crucial point is reproducibility [34; 35; 36].