Health

The AURA-CTP project investigates hidden privacy risks in clinical tumor profiling, focusing on whether tumor data can still expose patient identities. Combining pathology, bioinformatics, and data privacy expertise, it systematically tests re-identification attacks and assesses clinical implications.

This project explores the implications of using synthetic data in healthcare, focusing on liver research. It examines differences between synthetic and real-world data, evaluates generation approaches, and assesses the validity of conclusions drawn from both data sources. The interdisciplinary effort combines medical expertise and computer science to address critical gaps in synthetic data utility for healthcare.

AI-MED focuses on an agent-based AI system that combines radiological images with patient data to deliver more holistic medical diagnostics. Focusing on thoracic X-rays, it explores how Large Language Models can integrate imaging, laboratory results, and patient history for precise, privacy-preserving decision support.