Alaedine Benani : données synthétiques pour les Nuls (+ présentation du sujet de thèse)

Alaedine is a new PhD student at Limics, and will present his research subject, as well as a focus on synthetic health data.

This presentation explores the role of synthetic data as a promising solution to overcome regulatory barriers in healthcare machine learning. By detailing its methodology, including regulatory compliance, reduced re-identification risks, and usability for ML training, two key methods, Octopize and SDV (CT-GAN), will be examined to illustrate how synthetic data can replicate realistic datasets while addressing specific limitations. Practical applications within the Prīsm project and examples from scientific literature highlight the transformative potential of synthetic data in healthcare and beyond.