Background Ensuring safe adoption of AI tools in healthcare hinges on access to sufficient
data for training, testing and validation. Synthetic data has been suggested in response to …

The generation of synthetic data can be used for anonymization, regularization,
oversampling, semi-supervised learning, self-supervised learning, and several other tasks …

Clinical research relies on high-quality patient data, however, obtaining big data sets is
costly and access to existing data is often hindered by privacy and regulatory concerns …

Nowadays, the use of synthetic data has gained popularity as a cost-efficient strategy for
enhancing data augmentation for improving machine learning models performance as well …

Introduction Sharing and re-using health-related data beyond the scope of its initial
collection is essential for accelerating research, develo** robust and trustworthy machine …

The success of Large Language Models (LLMs) is inherently linked to the availability of vast,
diverse, and high-quality data for training and evaluation. However, the growth rate of high …

Time series data are widely used and provide a wealth of information for countless
applications. However, some applications are faced with a limited amount of data, or the …

The development of healthcare patient digital twins in combination with machine learning
technologies helps doctors in therapeutic prescription and in minimally invasive intervention …

The rapid advancement of data generation techniques has spurred innovation across
multiple domains. This comprehensive review delves into the realm of data generation …

The potential of realistic and useful synthetic data is significant. However, current evaluation
methods for synthetic tabular data generation predominantly focus on downstream task …