AG Vieluf

The AI-based Telemonitoring research group focusses on the integration of artificial intelligence (AI) with clinical practice. As telemedicine continues to evolve, the amount of valuable patient data has significantly increased, opening new opportunities for AI-driven insights to enhance patient care. Our research aims to leverage these data streams, combining cutting-edge AI techniques with clinical expertise.

Our team focuses on applications in cardiology but also collaborates across multiple disciplines, including neurology, psychology, sport science, bioinformatics, statistics, and computer science. This interdisciplinary approach enables us to conduct both foundational and applied research, focusing on the practical application of AI in real-world clinical settings.

Key research areas include:

• AI for Wearables: Utilizing machine learning to analyze data from wearable devices, providing predictive insights for the early detection and management of cardiovascular and neurological diseases.
• Multimodal ML for Medicine: Using machine learning to analyze clinical data of different modalities including electronic health records (EHRs), medical imaging, wearable sensors, and genomic data.
• Cardiovascular Image Analysis: Developing AI models to improve the accuracy and efficiency of cardiovascular imaging, supporting precise diagnostics and personalized treatment plans.
• Explainable AI in Clinical Practice: Advancing explainable AI techniques to ensure that machine learning models provide transparent, interpretable results that clinicians can trust and apply in decision making.

We specialize in using a variety of advanced machine learning methods, with a strong emphasis on explainability and reproducibility ensuring that our methodologies can be easily replicated and scaled for use in different clinical environments. Our research aims at integrating AI tools seamlessly into clinical workflows, offering user-friendly solutions that align with the needs of healthcare providers and patients.