Professor Daniel Rückert stands as a preeminent figure in the application of artificial intelligence to healthcare and medical diagnostics. He currently holds the Alexander von Humboldt Professorship for AI in Medicine and Healthcare at the Technical University of Munich, where he serves as Director of the Institute for AI and Informatics in Medicine, while maintaining his position as Professor of Visual Information Processing at Imperial College London. After completing his computer science studies at TU Berlin in 1993, he earned his doctorate from Imperial College London in 1997 with groundbreaking research on cardiovascular image segmentation and tracking. His distinguished career trajectory includes a postdoctoral fellowship at King's College London, followed by progressive appointments at Imperial College London, where he served as Head of the Department of Computing from 2016 to 2020 before assuming his prestigious Humboldt Professorship at TUM in 2020.
Rückert's pioneering research has fundamentally transformed medical image computing through the development of innovative algorithms for image acquisition, analysis, and interpretation. His work has significantly advanced critical areas including image registration, reconstruction, tracking, segmentation, and modeling, with particular emphasis on applying artificial neural networks to enhance medical imaging quality and diagnostic capabilities. Among his most impactful contributions is a method that accelerates MRI image reconstruction for fetal imaging without compromising image quality, addressing a longstanding clinical challenge. With over 500 publications and more than 128,000 citations (h-index 133), his research has established foundational methodologies now widely adopted in medical imaging research and clinical applications worldwide.
Professor Rückert has profoundly shaped the field through his leadership roles and commitment to advancing reliable AI systems in healthcare. His election as Fellow to numerous prestigious academies—including the Royal Academy of Engineering (2015), IEEE (2015), Academy of Medical Sciences (2019), and the German National Academy of Sciences Leopoldina (2023)—attests to his significant cross-disciplinary impact. As a dedicated mentor, he has guided over 50 PhD students and supervised more than 40 postdoctoral researchers, cultivating the next generation of leaders in medical AI. His current research focuses on developing transparent, explainable, and reliable AI systems for medical applications, with particular emphasis on ensuring clinical reliability and addressing ethical considerations in healthcare AI deployment through his leadership at the Zuse School of Excellence in Reliable AI (relAI).
