Advanced Statistical Methods for Trustworthy Generative AI in Biomedical Imaging Studiestation

Thomas M. Menino Convention & Exhibition Center 

Generative AI has rapidly transformed the biomedical imaging field by enabling image synthesis, helping address challenges of limited data availability, privacy, and diversity in biomedical research. Yet, the adoption of AI-generated images in biomedical studies requires rigorous methods to ensure their reliability for downstream analysis. In this talk, I will introduce novel and rigorous nonparametric approaches that strengthen the trustworthiness and statistical validity of synthetic biomedical imaging data. We develop simultaneous confidence regions to rigorously quantify uncertainty and detect meaningful differences between synthetic and original imaging data. To further enhance fidelity and utility, we propose a transformation that aligns the mean and covariance structures of synthetic images with those of the originals. I will also discuss methods for imputing missing imaging phenotypes using generative models and demonstrate how joint analysis of observed and imputed traits enhances inference while accounting for imputation error. Extensive simulations and applications to brain imaging data validate the proposed framework, demonstrating how these methods empower rigorous statistical inference and promote trustworthy advances in biomedical imaging.

Trustworthy AI

Statistical Inference

Synthetic Data

Brain Imaging Studies