Generating infinitely scalable clinical datasets free from Protected Health Information (PHI) constraints.
Diffusion models and Wasserstein GANs trained on de-identified hospital data generate photoplethysmogram and EEG time series that match true population distributions without containing actual patient biometrics.
Mathematical differential privacy bounds ensure that membership inference attacks cannot deduce whether any individual patient's medical records were included in the training foundation.