AI, medical data and SwissCardIA

Abstract

Atherosclerotic cardiovascular disease represents the leading cause of death in Switzerland and in the world. Current preventive strategies rely on traditional risk models that lack precision and granularity. It is time to re-visit the approach and harness the advancements in AI.

The SwissCardIA project led by EPFL, CHUV, HUG, UNISANTE aims to revolutionize cardiovascular prevention by developing a dynamic, AI-driven risk prediction framework that integrates Wearable Device Data, Advanced Imaging, Biochemical Markers and Comprehensive Patient Information. The multi-modal data integration approach is aimed to enable early detection of cardiovascular disease at asymptomatic stages, well before clinical events such as myocardial infarctus occur.

About

Emmanuel Abbé received his Ph.D. degree from the EECS department at MIT. He was a professor at Princeton University in applied mathematics and is now a professor at EPFL, where he holds the Chair of Mathematical Data Science. He is the recipient of the the Bell Labs Prize, the von Neumann Fellowship from the Institute for Advanced Study, the IEEE Information Theory Society Paper Award, the ICML Outstanding Paper Award and the Frontiers of Science Award.  He is also a senior research scientist at Apple AIML. He mostly works on AI reasoning and AI for cardiology.

AMR-Initiative

Translational Neuromodeling & Computational Psychiatry

Abstract

Contemporary psychiatry faces major challenges. Diagnostic classifications are descriptive and agnostic about mechanisms. As a consequence, treatment is based on trial and error, and patients with the same diagnosis show highly variable treatment responses and clinical trajectories.

In recent years, computational approaches to addressing these problems have gained traction. This presentation summarises advances in developing computational assays of brain dysfunction (Translational Neuromodeling) and applying them to clinical problems (Computational Psychiatry). The clinical potential as well as practical challenges will be discussed.

About

Klaas Enno Stephan is a computational neuroscientist and medical doctor. He is currently Professor for Translational Neuromodeling & Computational Psychiatry at the University of Zurich and ETH Zurich. His work concerns the development of clinically useful computational assays for psychiatry and psychosomatic medicine, with a current focus on fatigue and depression.

AI in Medicine

Abstract

Artificial intelligence is rapidly transforming medicine, from accelerating biomedical research to supporting clinical decision-making. Advances in machine learning, particularly deep and multimodal representation learning, now enable the integration of diverse data sources such as imaging, genomics, and electronic health records. These capabilities open new possibilities for earlier diagnosis, personalized treatment, and more efficient healthcare delivery. At the same time, challenges around interpretability, safety, data quality, and clinical validation remain central to responsible deployment. This talk will provide an overview of ongoing research directions and real-world applications of AI in medicine.

About

Julia Vogt is an associate professor in Computer Science at ETH Zurich, where she leads the Medical Data Science Group. The focus of her research is on linking computer science with medicine, with the ultimate aim of personalized patient treatment. She has studied mathematics both in Konstanz and in Sydney and earned her Ph.D. in computer science at the University of Basel. She was a postdoctoral research fellow at the Memorial Sloan-Kettering Cancer Center in NYC and with the Bioinformatics and Information Mining group at the University of Konstanz. In 2019, she joined the Computer Science Department of ETH Zurich.

X-ray Imaging 2.0: leveraging multiscale, multimodal imaging at synchrotrons for improved diagnostic in hospitals

Abstract

More than 125 years after Röntgen’s discovery, X-ray imaging is entering a new era powered by 4^th generation synchrotron facilities. These ultra-bright sources let scientists peer inside objects with astonishing detail—down to tens of nanometers—at speeds up to thousands of 3D scans per second. This leap enables real-time views of living tissues, brain networks, dissolving tablets, and soft materials in motion. Techniques once built for accelerator diagnostic now promise breakthroughs in medicine, from breast to lung imaging. This talk opens the door to the fascinating world of modern X-ray imaging and its future impact on healthcare.

About

Marco Stampanoni is Full Professor of X-ray Imaging at ETH Zurich and Head of the X-ray Tomography Group at the Swiss Light Source (PSI). His research focuses on synchrotron-based imaging and phase-contrast techniques with applications in medicine and materials science. A pioneer in high-resolution tomography, he has received an ERC Grant and multiple international awards for advancing X-ray imaging. At ETH, he teaches X-ray microscopy and leads efforts to translate cutting-edge imaging technologies into clinical practice.

Water use, water reuse, and water and wastewater treatment

About

Eberhard Morgenroth holds a PhD from the Technical University of Munich (1998). In 2000 he joined the faculty of the University of Illinois at Urbana-Champaign. Since 2009 he is a Professor for Process Engineering in Urban Water Management with appointments at ETH Zürich and at Eawag. His research interests include wastewater treatment, membrane bioreactors for water reuse, control of biofilms, biofilm reactors, and decentralized wastewater management.