Daniel Truhn

Daniel Truhn is a physicist, physician and board-certified radiologist who directs the Lab for Artificial Intelligence in Medicine at University Hospital RWTH Aachen. His research brings machine learning into clinical practice, from medical imaging to multimodal and agent-based medical AI. He holds an ERC Starting Grant for the project SAGMA.

Daniel Truhn is a physicist, imaging scientist and clinical radiologist with a dedicated focus on machine learning and magnetic resonance imaging. After studying physics at RWTH Aachen University and Imperial College London, he went on to study medicine at RWTH Aachen University. In 2013 he completed his MD thesis on the compatibility of positron emission tomography and magnetic resonance imaging and joined the Department of Diagnostic and Interventional Radiology at University Hospital RWTH Aachen, where he became a board-certified radiologist in 2019. Alongside his clinical work he spent two years as a research fellow at the Institute of Imaging and Computer Vision (RWTH Aachen University) before returning to the clinic. Today he directs the Lab for Artificial Intelligence in Medicine. His research focuses on bringing machine-learning methods into clinical practice and on bridging the gap between research possibilities and clinical applicability. In 2025 he was awarded an ERC Starting Grant for SAGMA, an agent-based system for general medical AI.

Daniel Truhn (Univ.-Prof. Dr. med. Dipl.-Phys., M.Sc.) is a physicist, imaging scientist and clinical radiologist whose work sits at the intersection of machine learning and medicine. He studied physics at RWTH Aachen University and Imperial College London and subsequently medicine at RWTH Aachen University. In 2013 he completed his MD thesis on the compatibility of positron emission tomography and magnetic resonance imaging and joined the Department of Diagnostic and Interventional Radiology at University Hospital RWTH Aachen, where he became a board-certified clinical radiologist in 2019. In parallel he pursued his interest in machine learning as a research fellow at the Institute of Imaging and Computer Vision (RWTH Aachen University) for two years before returning to the clinic.

He now directs the Lab for Artificial Intelligence in Medicine at University Hospital RWTH Aachen, a young and interdisciplinary team of physicians, physicists and computer scientists. The lab develops deep-learning methods for clinical routine data across five research directions: multimodal AI, generative models such as diffusion models and GANs, large language models, cancer imaging and musculoskeletal imaging. A recurring theme of his work is translation: bringing methods from research into clinical practice and closing the gap between what is technically possible and what is clinically useful.

His group is funded by the German Research Foundation (DFG), the German Federal Ministry of Research, Technology and Space, the European Union and the European Regional Development Fund, and it contributes to large collaborative projects including ODELIA, DECIPHER-M, JAIF and KIMONA. In 2025 he was awarded an ERC Starting Grant for SAGMA – Synergistic Agents for General Medical AI, which develops an agent-based system in which specialised AI models collaborate under a central intelligent manager to support physicians with comprehensive, evidence-based analyses. His research has appeared in journals including Nature Machine Intelligence, npj Digital Medicine and Science Advances; a full publication list is available on Google Scholar.