Keynote Speakers of MICAD 2026

Prof. Vicente Grau

Oxford University, UK

Vicente Grau is a Professor of Biomedical Image Analysis at the Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, and a Professorial Fellow in Engineering at Mansfield College. Vicente holds a PhD in medical image analysis from Universidad Politécnica de Valencia, Spain. After spending two years at Brigham and Women’s Hospital, Harvard University, and the ONH Biomechanics Lab at LSU Health Sciences Center, he joined the University of Oxford in 2004. He was awarded the title of Professor in 2015.
Vicente’s research focuses on the development of artificial intelligence (AI)-enabled methods to improve the understanding of human anatomy and physiology and to provide solutions for healthcare. He is particularly interested in combining medical images with other information sources to build multimodal algorithms, as well as leveraging large databases for population-level studies. His research spans multiple clinical applications, with a special interest in cardiovascular medicine.

Prof. Bernhard Kainz

Imperial College London, UK

Prof. Bernhard Kainz is a Professor in the Department of Computing at Imperial College London. He leads the Human-in-the-Loop Computing Group and is one of four academics heading the Biomedical Image Analysis (BioMedIA) Lab. His research in human-in-the-loop computing focuses on integrating human intelligence with machine capabilities, and enhancing machine intelligence with human adaptability.
He collaborates closely with Imperial College Healthcare NHS Trust, King’s College London (Division of Imaging Sciences and Biomedical Engineering), St. Thomas’ Hospital London, and the Department of Bioengineering at Imperial College London. He serves as an Associate Editor for IEEE Transactions on Medical Imaging, Medical Image Analysis, and Machine Learning for Biomedical Imaging. In addition, he is a scientific adviser to ThinkSono Ltd and Ultromics Ltd, and a co-founder of Fraiya Ltd. He also serves as a stream lead for the EPSRC Centre for Doctoral Training in Smart Medical Imaging and the UKRI Centre for Doctoral Training in Artificial Intelligence for Healthcare.
His teaching focuses on real-time computing, deep learning, image analysis, and visual information processing.

Prof. Clarisa Sánchez Gutiérrez

University of Amsterdam, Netherlands

Clarisa, as full Professor of AI and Health at the University of Amsterdam, is deeply committed to advancing patient care through responsible AI technologies in healthcare. At the university, she holds positions at the Faculty of Science (IvI) and Amsterdam UMC (BMEP), fostering an interdisciplinary environment. In 2020, she co-founded the Quantitative Healthcare Analysis (qurAI) group, comprising over 20 PhD students and postdocs, dedicated to developing, validating, and integrating AI solutions for medical data challenges along the patient pathway. Her team focuses on cutting-edge technologies like generative AI, vision transformers, and self-supervised learning, while also prioritizing trustworthy AI aspects like interpretability and uncertainty estimation for medical data. Furthermore, Clarisa actively translates research outcomes into clinical applications, contributing to the global deployment of various software products. Notably, she co-invented CAD4TB, a CE certified AI software for TB screening operational in over 45 countries. Additionally, her group collaborates on the development of grand-challenge, an end-to-end platform for biomedical image analysis. Her multifaceted contributions underscore her commitment to advancing AI in healthcare and fostering collaboration across disciplines and sectors.

Prof. Emanuele Trucco

University of Dundee, UK

Emanuele (Manuel) Trucco, MSc, PhD, FRSA, FIAPR, FBMVA, is the NRP Chair of Computational Vision in Computing, School of Science and Engineering, at the University of Dundee, an Honorary Clinical Researcher of NHS Tayside, and a former Adjunct Professor at the Chinese Academy of Sciences. He was co-founder, NEX and scientific advisor for the spinout Eye to the Future.
He has been active since 1984 in computer vision, and since 2002 in medical image analysis, publishing more than 300 refereed papers and 2 textbooks. his research has been funded by UKRI (EPSRC, MRC), the EU, charities (incl. Royal Society, Wellcome Trust, Leverhulme) and industry (incl. Canon, Astra Zeneca, Lilly, OPTOS, British Aerospace). Manuel has served on the organizing or program committee of major international and UK conferences, including three times Program Chair for the British Machine Vision Conference, and co-General Chair for ECCV2020 (online, ~4,600 delegates). At MICCAI, Manuel has served as co-chair of several editions of the OMIA workshop (since 2015), Area Chair, and invited speaker at workshops (LABELS, CARE). Manuel is co-director of VAMPIRE (Vessel Assessment and Measurement Platform for Images of the Retina), an international research initiative led by the Universities of Dundee and Edinburgh (co-director Dr Tom MacGillivray), part of the UK Biobank Eye and Vision Consortium and of the Healthcare Data Research UK Scottish Institute. VAMPIRE software is being used around the world in biomarker studies on cardiovascular risk, stroke, dementia, diabetes and complications, cognitive performance, neurodegenerative diseases, and genetics.

Dr. Daguang Xu

Senior Research Manager, NVIDIA, USA

Dr. Daguang Xu is a Senior Research Manager in Healthcare at NVIDIA, where he leads AI research in healthcare. His work spans medical imaging analysis, multimodal foundation models, and, more recently, physical AI and world models. His team is the primary contributor to the open-source platforms MONAI, and is increasingly focused on releasing open foundation models, and open datasets for generative AI in healthcare. Dr. Xu received the PhD degree from Johns Hopkins University. Before joining NVIDIA, he worked as a research scientist at Siemens Healthineers.

Speech Title: Toward Agentic Healthcare AI: Foundation Models for Perception, Reasoning, and Synthesis

Aabstract: Generative AI is rapidly transforming healthcare by enabling new paradigms for perception, reasoning, reconstruction, and data synthesis across end-to-end clinical workflows. In this talk, I will present recent research advances and open releases from NVIDIA on building healthcare-specific foundation models and curated datasets that support scalable, multimodal, and increasingly agentic AI systems. These efforts span core capabilities including segmentation for perception and annotation, vision–language modeling for interpretation and prediction, image reconstruction, and generative synthesis for simulation and data augmentation—collectively enabling more efficient, robust, and generalizable solutions for real-world clinical and research applications.