Advances in imaging devices have drastically increased our capabilities to (non-invasively) study both anatomy and function non-invasively. In addition, imaging data are increasingly complemented with other types of data, including genetic and metabolomics data. With these advances, the sheer size, complexity, and heterogeneity of biomedical (imaging) data have increased enormously, and the challenges to optimally use this information for biomedical research and clinical practice have increased accordingly. At the same time, methods for the automated analysis of these data have increased tremendously. Especially the analysis of biomedical (imaging) data with artificial intelligence techniques will have an enormous impact on disease prevention, cure and care, and by 2030 it will have dramatically changed the landscape of the healthcare system. This research group is at the forefront of these developments; its focus is to develop advanced image processing and machine learning techniques to analyse large biomedical imaging resources and clinical imaging data with the aim to develop novel diagnostic, prognostic, therapy planning and therapy monitoring tools. In addition, the group develops methods for linking image-derived features to other (e.g. genetic data) and methods for the integrated analyses of these complementary data to understand disease aetiology and investigate the potential for disease staging and prognosis using such analyses.