  Individual health data is crucial for scientific advancements, particularly
in developing Artificial Intelligence (AI); however, sharing real patient
information is often restricted due to privacy concerns. A promising solution
to this challenge is synthetic data generation. This technique creates entirely
new datasets that mimic the statistical properties of real data, while
preserving confidential patient information. In this paper, we present the
workflow and different services developed in the context of Germany's National
Data Infrastructure project NFDI4Health. First, two state-of-the-art AI tools
(namely, VAMBN and MultiNODEs) for generating synthetic health data are
outlined. Further, we introduce SYNDAT (a public web-based tool) which allows
users to visualize and assess the quality and risk of synthetic data provided
by desired generative models. Additionally, the utility of the proposed methods
and the web-based tool is showcased using data from Alzheimer's Disease
Neuroimaging Initiative (ADNI) and the Center for Cancer Registry Data of the
Robert Koch Institute (RKI).
