From Models to Medicine - Theoretical Foundations and Practical Impact of Machine Learning in Healthcare

Abstract:

The fusion of artificial intelligence and medicine is driving a healthcare transformation, enabling personalized treatment tailored to each patient’s unique needs. By leveraging advances in machine learning, we can transform challenging and diverse patient data into actionable insights that have the potential to reshape clinical practice. In this presentation, I will cover both the theoretical foundations and practical applications of machine learning in healthcare. I will highlight innovative approaches designed to enhance diagnostics, improve patient care, and make medical decision-making more accessible and reliable. Through real-world medical examples, I aim to illuminate the collaborative future of machine learning and healthcare, where cutting-edge computational techniques synergize with medical expertise to pave the way for decision support systems truly integrated into medical practice.

About Julia:

Julia Vogt is an assistant 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 2018, she joined the University of Basel as an assistant professor. In May 2019, she and her lab moved to Zurich where she joined the Computer Science Department of ETH Zurich. https://mds.inf.ethz.ch/team/detail/julia-vogt/