One major obstacle in current medicine and drug development is inherent in the way we define and approach diseases. Here, we will discuss the diagnostic and prognostic value of (multi-)omics panels in general. We will have a closer look at breast cancer survival and treatment outcome, as case example, using gene expression panels - and we will discuss the current "best practice" in the light of critical statistical considerations. Afterwards, we will introduce computational approaches for network-based medicine. We will discuss novel developments in graph-based machine learning using examples ranging from Huntington's disease mechanisms via Alzheimer's drug target discovery back to where we started, i.e. breast cancer treatment optimization - but now from a systems medicine point of view. We conclude that systems medicine and modern artificial intelligence open new avenues to shape future medicine.