## 4 Discussion
Ensuring consistency in the model architecture, hyperparameters, and random seed makes WGANs reproducible. We conducted in-depth hyperparameter tuning to optimize the performance of WGANs. Various parameter combinations were tested to evaluate the algorithm. It is also vital to prevent hallucinations of generated data. In this study, we took all necessary measures to ensure the entire process is fully reproducible [37]. Nevertheless, the regulatory authorities should establish specific criteria and guidelines for the application of AI-generated virtual subjects in practice [38; 39; 40]. Finally, the importance of data augmentation has been recognized for many years. In this context, techniques like bootstrapping have routinely been used to increase sample sizes in clinical trials, but they have limitations such as overfitting, bias, loss of information, and assuming independence [41]. These restrictions are due to the fact that the same subjects are used, unlike the synthesis of new data as in the case of generative AI algorithms (e.g., WGANs). Notably, GANs find extensive applications across diverse domains. For example, in medical imaging, they are instrumental in generating synthetic MRI images for brain tumor detection [42]. In biomedical signal processing, GANs enhance the detection of epileptic activity through signal augmentation, while in computer vision, they can perform tasks like speech emotion recognition [43; 44]. GANs also play a pivotal role in data augmentation, improving machine learning model performance across domains like medical imaging for autism detection [45]. Recently, GANs have been used for security and privacy applications, generating synthetic data to anonymize sensitive information, creating adversarial examples for evaluating model robustness, and synthesizing realistic images for training surveillance systems [46]. It is noteworthy that GANs contribute to entertainment and creativity, generating artistic content including artwork, music, and literature, thereby fostering new avenues for artistic expression and creativity [47]. The need for rigorous validation and comprehensive ethical review of the virtual data generated by any generative AI algorithm should be highlighted. The validation process, among others, should include the following steps: comparison with real-world data, performance benchmarks, and external validation. For the first step, it is necessary to conduct extensive validation studies by comparing the characteristics of the synthetic data with actual clinical trial data to ensure fidelity and representativeness. This will involve statistical analyses to assess distribution, variance, and other key metrics. For the second step, the generative model should be benchmarked against established datasets, so its performance can be evaluated using industry-standard metrics to confirm its accuracy and reliability. The third step will involve collaboration with independent researchers to perform external validation of the synthetic datasets, ensuring objectivity and transparency in the validation process [48]. Furthermore, ethical review must be approved by the Institutional Review Board (IRB). Prior to any implementation in clinical settings, the generated data and the processes involved should undergo thorough review and approval by an IRB or an equivalent ethics committee. There must also be compliance with regulations to ensure that the computational methods comply with all relevant guidelines, such as the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), to protect patient privacy and data security. Finally, detailed documentation of the data generation process, including the algorithms used and the steps taken to validate and secure the data, should be maintained, and made available for review by ethical committees and regulatory bodies [49].