Big data analytics methods for the large scale analysis of imaging, genetic, laboratory, and clinical data have great potential to improve our understanding of disease, and to improve disease diagnosis and prognosis. Both classical machine learning (e.g. radiomics, multi feature classification) and deep learning methods are currently used in these domains. In this talk, I will present the results and challenges for both approaches to make impact in the context of a number of applications. Specifically, we will discuss early and differential diagnosis and improved prognosis of dementia, and improved neuro tumor characterization and treatment response prediction.