Recent progress in artificial intelligence and machine learning has led to the growth of
research in every aspect of life including the health care domain. However, privacy risks and …

Privacy protection for health data is more than simply strip** datasets of specific
identifiers. Privacy protection increasingly means the application of privacy-enhancing …

Background Deep learning (DL) is one of the latest approaches to artificial intelligence. As
an unsupervised DL method, a generative adversarial network (GAN) can be used to …

Data is a major asset in today's healthcare scenery. Hospitals are one of the primary
producers of healthcare-related data and the value this data can provide is enormous …

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 …

Digital health applications can improve quality and effectiveness of healthcare, by offering a
number of new tools to users, which are often considered a medical device. Assuring their …

After being collected for patient care, Observational Health Data (OHD) can further benefit
patient well-being by sustaining the development of health informatics and medical …

Data sharing is a necessity for innovative progress in many domains, especially in
healthcare. However, the ability to share data is hindered by regulations protecting the …

The generation of synthetic tabular data has emerged as a critical task in various fields,
particularly in healthcare, where data privacy concerns limit the availability of real datasets …

In this paper, an unsupervised generative modeling method that generates synthetic images
with the help of Deep Convolutional Generative Adversarial Networks (DCGANs). A method …