Professor Daniel Rueckert
Director, Institute for AI and Informatics in Medicine
Technical University Munich

Abstract:
Artificial intelligence (AI) methods have the potential to revolutionize the domain of medicine, for example, in medical imaging, where the application of advanced machine learning techniques, in particular, deep learning, has achieved remarkable success. However, the broad application of AI techniques in medicine is currently hindered by limited dataset availability for algorithm training and validation, partly due to legal and ethical requirements to protect patient privacy. Here, we present an overview of current and next-generation methods for federated, secure and privacy-preserving artificial intelligence with a focus on medical imaging applications, alongside potential attack vectors and future prospects in medical imaging and beyond.

Bio:
Daniel Rückert is Alexander von Humboldt Professor for AI in Medicine and Healthcare at the Technical University of Munich. He is also a Professor at Imperial College London. He is a Fellow of the Royal Academy of Engineering, a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), and a Fellow of the Academy of Medical Sciences. His research focuses on the development of innovative algorithms for biomedical image acquisition, image analysis and image interpretation, and in AI for extracting clinically useful information from biomedical images – especially for computer-assisted diagnosis and prognosis.

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