Generative Models for Synthetic RCT Subjects

The patent creates digital twins (synthetic subjects) that copy and replicate the characteristics, outcomes, and responses of human subjects in the RCT. These digital twins are generated using machine learning models trained on trial data and can be repeatedly simulated to provide additional statistical power without enrolling more human participants, thus resolving the contradiction between reducing time/cost and maintaining accuracy.

The patent adds a digital/synthetic dimension to the traditional physical human subjects in RCTs. By creating a parallel digital representation layer that mirrors human subject characteristics and responses, the system effectively doubles the sample size without increasing physical subject recruitment, thereby maintaining statistical power while reducing time and cost.

The patent dynamically adjusts the characteristics and parameters of digital twins based on the specific trial population being studied. The machine learning models are retrained or fine-tuned for each trial, and digital twin characteristics are adjusted to match the actual trial demographics, ensuring accuracy is maintained while still reducing the number of human subjects needed.

The system creates a dynamic adaptation mechanism where digital twins are continuously adjusted and refined to match the specific characteristics of each trial population. This dynamic process ensures that the synthetic subjects accurately represent the current trial demographics rather than relying on static historical data, thereby maintaining accuracy while reducing subject numbers.