## 4 Discussion
Moreover, using real data can reveal unforeseen challenges and factors that simulations might overlook, leading to more robust and generalizable conclusions. Therefore, incorporating real data is essential to move from theoretical exploration to practical implementation and to ensure the relevance and impact of the research findings. In this work, VAEs are proposed as a method to augment data in clinical trials, specifically to potentially reduce the necessary sample size. VAEs extend traditional autoencoders by utilizing an encoder network that maps input data to a multivariate normal distribution instead of a fixed point. Essentially, VAEs aim to establish a relationship between input data and a probability distribution across the latent space [26]. The adoption of VAEs brings numerous benefits. They have been recognized as a highly effective approach for developing generative models, which generate new synthetic or artificial data based on existing data. A significant advantage of VAEs over conventional autoencoders is their capability to generate new data from the same underlying distribution as the input data by sampling from this embedded distribution. These distinctive characteristics were used in this study to effectively reduce the actual sample size of a BE study. Significantly more work needs to be carried out on the integration of AI algorithms in the BE assessment of highly variable drugs. This study is only the first step towards this goal. Future studies should explore additional clinical designs commonly used for highly variable drugs, such as the semi-replicate 3 \(\times\) 3 or replicate designs like 2 \(\times\) 4. It is important to highlight that VAE-synthesized data cannot be utilized for safety assessments, as these require actual human data, but only as a means for increasing the statistical power of the study. The use of generative AI algorithms in clinical research necessitates rigorous validation and comprehensive ethical review of the synthetic data they produce [18]. The validation process should encompass comparison with real-world data, performance benchmarks, and external validation. Initially, extensive studies should compare synthetic data characteristics to actual clinical trial data, utilizing statistical analyses to ensure fidelity and representativeness. Subsequently, the generative model should be validated against established datasets using industry-standard metrics to confirm accuracy and reliability. Collaboration with independent researchers for external validation is essential to ensure objectivity and transparency. Detailed documentation of the data generation and validation process should be available for review by ethical committees. Ethical considerations also include obtaining consent for using clinical data in generating synthetic datasets and addressing potential biases to ensure fairness and representativeness. Adhering to these steps will ensure that the synthetic data generated by generative AI algorithms are scientifically valid and ethically sound for clinical research [21]. As AI continues to advance and its applications in healthcare proliferate, its value as a tool becomes increasingly evident [21; 30; 31; 32; 33; 34]. In our approach of using AI to virtually increase the sample size of BE studies, the benefits in terms of enhanced statistical power while using fewer human subjects become evident. However, it is important to emphasize that the proposed methodology does not advocate for the complete replacement of subjects in BE studies but rather supplements them. It is crucial to have an appropriate and representative sample size of actual human subjects to generate synthetic ones. This study demonstrates that VAEs can reduce the originally required sample size by up to 50% while maintaining or even increasing statistical power.