Jump to page content

Anonymised data for the digitalised medicine of the future


The intelligent use of data is driving innovation and competition across various industries. However, there is a tension between utilizing sensitive medical data and maintaining privacy. Ethical considerations, robustness, and transparency are vital factors to address. ANONY-MED is a research project that aims to develop an AI-based toolbox for preserving privacy in medical data synthesis, using generative AI methods such as GANs. This creates a foundation for developing and applying AI models in various use cases such as radiology, cardiology, and stroke therapy.


The main objectives of the project are to develop a privacy-preserving data synthesis method for medical data, incorporating generative AI methods like GANs and homomorphic encryption, validate and evaluate the usability of the synthesized data in AI models, ensure ethics, robustness, and transparency in the AI models, and develop a trustworthy AI framework for the project.

Funder and Cooperation Partner