Imagine you need a huge library of medical records to train a smart computer, but you can't use real patient files because of privacy laws. This project creates a 'digital twin' of that data—fake records that look and act like real ones but don't belong to any actual person. It's like using a high-quality flight simulator instead of a real plane to train pilots without any risk.
Medical AI development is slowed down by strict privacy laws and a lack of high-quality, unbiased datasets. Companies struggle to train robust models without risking patient data leaks or facing GDPR penalties.
A federated prototype platform featuring a modular containerized backend and a human-centered frontend. It includes modules for data synthesis, anonymization, and a meta-engine for auditing AI model quality.
If you are an AI developer dealing with a lack of diverse patient data for training — this project developed a synthetic data platform that provides trustworthy, unbiased datasets to make your algorithms ready for the real world.
If you are a hospital network dealing with strict GDPR rules that block data sharing — this project developed federated technologies that allow secure analysis of private data across borders without moving the actual files.
If you are a research firm dealing with biased datasets that lead to unreliable models — this project developed a machine-learning meta-engine that identifies model breaking points and supplies on-demand synthetic data to fix them.
Based on available project data, no specific pricing or commercial cost model is mentioned; it is currently an EU-funded research project.
The project focuses on a scalable data platform and uses modular containerization to ensure the system can handle various use cases across local, national, and cross-border settings.
Based on available project data, specific licensing terms are not provided, though the project involves a consortium of 16 partners including 7 industry players.
The platform uses anonymization techniques, attribute-based privacy measures, and federated technologies to ensure all data usage respects GDPR requirements.
The project includes a human-centered frontend and is being validated against real-world use cases to ensure usability for clinical professionals and data engineers.
The consortium is heavily weighted toward commercial application, with a 44% industry ratio consisting of 7 companies (including 4 SMEs). With 16 partners across 8 countries, the project balances academic research (4 universities, 4 research centers) with practical industrial deployment, suggesting a strong push toward a marketable product rather than just a theoretical paper.
Contact Associacao Fraunhofer Portugal Research
Contact us to explore licensing opportunities for synthetic medical data tools.
