One of the major tasks of phosphoproteomics is providing potential biomarkers for either diagnosis or drug targets in medical applications. Because most complex diseases are due to the actions of multiple genes/proteins, the identification of complex phospho‐signatures containing multiple phosphorylation events within phosphoproteomics‐based networks generates more efficient and robust biomarkers than a single, differentially phosphorylated substrate or site. Here, we briefly summarize the current efforts and progress in this newly emerging field of phosphoproteomics‐based network medicine by reviewing the computational (re)construction of phosphorylation‐mediated signaling networks from unannotated phosphoproteomic data, the discovery of robust network phospho‐signatures and the application of these signatures for classifying cancers and predicting drug responses. The challenges as well as the potential advantages are evaluated and discussed. Although the current techniques are at present far from mature, we believe that such a systematic approach as we describe can generate more useful and robust biomarkers for biomedical usage, even at the current stage of development.