Precision medicine (PM) is an important modern paradigm for combining new types of metrics with big medical datasets to create prediction models for prevention, diagnosis, and specific therapy of chronic diseases. The aim of this paper was to differentiate PM from personalized medicine, to show potential benefits of PM for managing chronic diseases, and to define problems with implementation of PM into clinical practice. PM strategies in chronic airway diseases, diabetes, and cardiovascular diseases show that the key to developing PM is the addition of big datasets to the course of individually profiling diseases and patients. Integration of PM into clinical practice requires the reengineering of the health care infrastructure by incorporating necessary tools for clinicians and patients to enable data collection and analysis, interpretation of the results, as well as to facilitate treatment choices based on new understanding of biological pathways. The size of datasets and their large variability pose a considerable technical and statistical challenge. The potential benefits of using PM are as follows: 1) broader possibilities for physicians to use the achievements of genomics, proteomics, metabolomics, and other "omics" disciplines in routine clinical practice; 2) better understanding of the pathogenesis and epidemiology of diseases; 3) a revised approach to prevention, diagnosis, and treatment of chronic diseases; 4) better integration of electronic medical records as well as data from sensors and software applications in an interactive network of knowledge aimed at improving the modelling and testing of therapeutic and preventative strategies, stimulating further research, and spreading information to the general public.