## 4 Discussion
Furthermore, contemporary studies utilize deep learning approaches in image analysis for personalized medicine, as well as for the detection of hazardous objects in X-ray security inspection devices [19; 20]. Besides the extensive utilization of AI in diagnostic approaches, AI has also been integrated into various medical fields, including pneumomology, neurology, cardiology, gynecology, anesthesiology, surgery, urology, etc. [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39]. Data augmentation, utilized in various fields such as computer vision to assist AI models in better performing their tasks, has gained recognition in clinical trials [7; 40]. In the same vein, this study, along with the previous one [10], attempts to apply state-of-the-art techniques in order to address an old problem in clinical trials, namely, to reduce sample sizes in clinical trials and bioequivalence studies. To address the need for recruiting large sample sizes, particularly in cases involving highly variable drugs or drug products, this study introduces the concept of reducing human exposure by using only a limited sample size from which virtual subjects can be generated using AI. Certainly, caution should be exercised in selecting the actual subjects, as the original sample size must be informative. The advantages of applying this idea in practice include a decrease in human exposure (for ethical reasons), significantly shorter study completion times, lower complexity in the clinical trial, reduced workload for physicians and clinics, clinical research associates monitoring the study, and, overall, significantly lower costs for sponsors or health agencies. In general, this study demonstrates that less data can be used to achieve similar, and even better, results in terms of statistical power. Up to this point, it is essential to emphasize several important aspects of the proposed methodology. Firstly, the use of synthetic data in clinical trials aims solely to increase the study statistical power. This enhancement is achieved by utilizing VAEs to generate virtual subjects based on information obtained from the actual human subjects. The concept introduced in this paper suggests for the use of VAEs not as a complete replacement for human volunteers but, rather, as a partial substitute. The objective is to reduce human exposure in clinical trials by increasing statistical power without requiring additional human subjects, thus circumventing associated issues like ethical concerns, costs, and time constraints [41]. Thus, AI-generated virtual subjects are used for this purpose. Therefore, having an adequate sample of human volunteers, both representative and sufficiently large, is crucial for accurately generating virtual subjects. However, it is important to note that VAE-synthesized data cannot be used for safety assessments. Additionally, the utilization of VAEs in BE studies offers another advantage related to their ability to reduce variability inherently [42]. By using VAEs, it becomes feasible to generate virtual subjects with diminished variability, thereby narrowing the 90% confidence interval and enhancing the likelihood of establishing bioequivalence (i.e., increasing statistical power). Currently, the use of scaled bioequivalence limits can lead to inflation of type I error [6]. Thus, our approach enables comparable results to be achieved without encountering any type I error inflation. Reproducibility is another critical aspect to consider. VAEs can be rendered reproducible by maintaining consistency in the model architecture, hyperparameters, and random seed [42; 43; 44]. In the same context lies the need for avoiding hallucinations of the generated data [45]. In our study, we ensured all these measures, ensuring the entire process is fully reproducible. It is up to the regulatory authorities to set specific criteria and guidelines on the minimum requirements for the application of AI-generated virtual subjects in practice.