Large-scale data analysis for next generation health care

Modern medicine increasingly relies on high-dimensional data – genomic, proteomic, imaging, and continuous physiological signals from clinical systems and wearables – that must be turned into reliable, individualized insight. The MedLab develops mathematical and machine-learning methods that make this possible: models that are not only accurate but interpretable, so that clinicians and patients can trust and act on their results. This spans the identification of multivariate disease signatures across -omics and imaging data, the construction of multi-scale models that embed these signatures in their biological context, and the extension of point-wise clinical analysis toward continuous monitoring of large mobile-health data streams – enabling earlier diagnosis, better therapy monitoring, and real-time tools for high-risk patients.

Safe and interpretable AI for biomedical applications

Artificial intelligence is transforming healthcare and the adjacent biomedical and medtech industries – raising both the potential and the expectations for what data-driven methods can deliver. This dynamic demands innovative approaches not only for developing AI-based methods for critical, sensitive domains, but also for continuously validating and monitoring them once deployed. Building on two prior funding phases, in which the MedLab developed methods for capturing and exploiting biomedical data and established the technological infrastructure needed to analyze very large datasets, the third phase pursues two goals in parallel: developing AI-based services for concrete biomedical use cases with our industry partners, and building a platform for the continuous validation, monitoring, and certification-readiness of these AI systems – increasingly relevant in light of the EU AI Act’s requirements for high-risk AI systems.