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 …

Synthetic data provides a privacy protecting mechanism for the broad usage and sharing of
healthcare data for secondary purposes. It is considered a safe approach for the sharing of …

Synthetic health data have the potential to mitigate privacy concerns in supporting
biomedical research and healthcare applications. Modern approaches for data generation …

Synthetic data generation is the process of using machine learning methods to train a model
that captures the patterns in a real dataset. Then new or synthetic data can be generated …

Electronic healthcare record data have been used to study risk factors of disease, treatment
effectiveness and safety, and to inform healthcare service planning. There has been …

Data are central to research, public health, and in developing health information technology
(IT) systems. Nevertheless, access to most data in health care is tightly controlled, which …

Synthetic data is a rapidly evolving field with growing interest from multiple industry
stakeholders and European bodies. In particular, the pharmaceutical industry is starting to …

Background Synthetic data may provide a solution to researchers who wish to generate and
share data in support of precision healthcare. Recent advances in data synthesis enable the …

Synthetic data generation (SDG) research has been ongoing for some time with promising
results in different application domains, including healthcare, biometrics and energy …

There is a growing demand for the uptake of modern artificial intelligence technologies
within healthcare systems. Many of these technologies exploit historical patient health data …