About the Speaker
Daniel Rückert is Alexander von Humboldt Professor for AI in Medicine and Healthcare at the Technical University of Munich, directing the Institute for AI and Informatics in Medicine. He is also a Professor in the Department of Computing at Imperial College London, where he was Head of Department from 2016 to 2020. He earned a Diploma in Computer Science from the Technical University Berlin and a Ph.D. from Imperial College London, and was a postdoctoral fellow at King’s College London.
He has published over 500 journal and conference articles, supervised more than 50 PhD students, and serves on several editorial boards including IEEE Transactions on Medical Imaging and Medical Image Analysis. He is a Fellow of MICCAI, the Royal Academy of Engineering, IEEE, the Academy of Medical Sciences, ELLIS, AIMBE, and the German National Academy of Sciences Leopoldina. His awards include ERC Synergy Grant (2013), ERC Advanced Grant (2020), Alexander von Humboldt Professorship (2020), MICCAI Enduring Impact Award (2024), and the Gottfried Wilhelm Leibniz Prize (2025).
His research advances biomedical imaging and health modelling through artificial intelligence. He develops AI-driven inverse modelling methods to enhance imaging performance and builds multimodal AI models that integrate diverse patient data for a comprehensive understanding of health and disease. Combining these with biophysical and clinical knowledge, his work aims to create accurate, interpretable, and responsible AI systems. He focuses on translating these methods to improve diagnosis and stratification in neurological and cardiovascular diseases.
Talk Abstract
Artificial Intelligence (AI) is changing many fields across science and our society. This talk will discuss how AI is changing medicine and healthcare, particularly in radiology. I will focus on how AI can support the acquisition of medical images and image analysis and interpretation. This can enable the early detection of diseases and support the improved personalised diagnosis. I will show several examples of this in the talk, including cardiovascular MR imaging. Furthermore, we will discuss how AI solutions can be privacy-preserving while also providing trustworthy and explainable solutions for clinicians.
We are looking forward to the talk!