Recent advancements in generative approaches in AI have opened up the prospect of
synthetic tabular clinical data generation. From filling in missing values in real-world data …

Anonymization is crucial for the sharing of personal data in a privacy-aware manner yet it is
a complex task that requires to set up a trade-off between the robustness of anonymization …

An external control arm based on health registry data can serve as an alternative
comparator in single-arm drug development studies that lack a benchmark for comparison to …

Aims Data availability remains a critical challenge in modern, data-driven medical research.
Due to the sensitive nature of patient health records, they are rightfully subject to stringent …

Deep generative models and synthetic medical data have shown significant promise in
addressing key challenges in healthcare, such as privacy concerns, data bias, and the …

Although the advance of Artificial Intelligence (AI) in medicine is significant, its potential
impact depends on access to quality data. A particular challenge is class imbalance in …

Health data represents a large volume of information, generated daily and sensitive by
nature. However, sharing this data is essential for advancing research and, ultimately …

Sharing personal health data can boost scientific research, in terms of increased
comprehension of diseases, diagnostics, treatments, and predictive capabilities. But …

In an increasingly digital world, the protection of personal data is paramount for individuals,
organizations, and regulators. As data collection technologies evolve, so must methods for …

The integration of medical open databases with artificial intelligence (AI) technologies marks
a transformative era in biomedical research and healthcare innovation. Over the past 25 …