Implementation of a Generative AI Algorithm for Virtually Increasing the Sample Size of Clinical Studies

Abstract

Determining the appropriate sample size is crucial in clinical studies due to the potential limitations of small sample sizes in detecting true effects. This work introduces the use of Wasserstein Generative Adversarial Networks (WGANs) to create virtual subjects and reduce the need for recruiting actual human volunteers. The proposed idea suggests that only a small subset (“sample”) of the true population can be used along with WGANs to create a virtual population (“generated” dataset). To demonstrate the suitability of the WGAN-based approach, a new methodological procedure was also required to be established and applied. Monte Carlo simulations of clinical studies were performed to compare the performance of the WGAN-synthesized virtual subjects (i.e., the “generated” dataset) against both the entire population (the so-called “original” dataset) and a subset of it, the “sample”. After training and tuning the WGAN, various scenarios were explored, and the comparative performance of the three datasets was evaluated, as well as the similarity in the results against the population data. Across all scenarios tested, integrating WGANs and their corresponding generated populations consistently exhibited superior performance compared with those from samples alone. The generated datasets also exhibited quite similar performance compared with the “original” (i.e., population) data. By introducing virtual patients, WGANs effectively augment sample size, reducing the risk of type II errors. The proposed WGAN approach has the potential to decrease costs, time, and ethical concerns associated with human participation in clinical trials.