Generating Benchmark Health Data Using a Tabular Diffusion Transformer

Healthcare researchers often need synthetic data spanning multiple related but heterogeneous tables, yet most generative models only handle single tables. This paper's two-stage framework first standardizes diverse tables into common statistical summaries capturing distributions and correlations, then uses a diffusion transformer to generate new synthetic statistical tables, which are reconstructed back into realistic raw data. This enables privacy-preserving generation of unlimited realistic multi-table datasets. Applications include healthcare data sharing without exposing patient records, benchmark creation for medical AI research, and synthetic data generation for any domain with complex relational tables. Authors: Hao Yan, Lisa Pilgram, Dan Liu, Linglong Kong, Fida Dankar, Khaled El Emam Paper: https://arxiv.org/abs/2608.14496v1