The research group "AI-based Telemonitoring" focuses on the integration of artificial intelligence (AI) into clinical practice. With the advancement of telemedicine, the amount of valuable patient data has increased significantly, opening up new opportunities for AI-powered insights to improve patient care. Our research aims to harness these data streams and combine cutting-edge AI techniques with clinical expertise.

Our team focuses on applications in cardiology, but also collaborates across disciplines, including neurology, psychology, sports science, bioinformatics, statistics and computer science. This interdisciplinary approach allows us to conduct both basic and applied research, with a focus on the practical application of AI in real clinical settings.

Key research areas include:

• AI for wearables: using machine learning to analyze data from wearable devices to provide predictive insights for the early detection and treatment of cardiovascular and neurological diseases.

• Multimodal ML for medicine: using machine learning to analyze clinical data from multiple modalities, including electronic health records (EHR), medical imaging, wearable sensors and genomic data.
• Cardiovascular image analysis: developing AI models to improve the accuracy and efficiency of cardiovascular imaging to support accurate diagnoses 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 use in decision making.

We specialize in the use of a variety of advanced machine learning methods, with a strong emphasis on explainability and reproducibility to ensure that our methods can be easily replicated and scaled for use in different clinical settings. Our research aims to seamlessly integrate AI tools into clinical workflows and provide user-friendly solutions that meet the needs of healthcare providers and patients.