The field of systems medicine aims to develop mechanistic disease definitions and to identify groups of patients that would benefit from targeted treatment with existing, new or repurposed drugs. The basis for systems medicine are big biomedical data obtained through modern omics technologies as well as rich public databases on molecular interactions, comorbidities, drug effects, etc. Networ-based integrative artificial intelligence (AI) methods have the potential to leverage these big data to change future clinical decision-making. We will highlight emerging examples of big data in systems medicine ranging from de novo endophenotyping, i.e. the stratification of patients based not only on simple molecular markers, but on network-based markers to network-based methods for detecting aberrant splicing.