Medical practice is undergoing a revolution around personalized health: this major change is driven by the continuous development of cost-effective high-throughput technologies that produce gigantic quantities of data in numerous areas, from imaging to genomics, and of the corresponding tools required to process these data (Rev Med Suisse 2016; 12: 414-6). These will allow the sketching of a very specific and personal portrait of each individual’s state of health. Those developments are expected on one hand to boost basic research, and on the other hand to greatly assist clinicians in diagnosis, prognosis, delaying or preventing diseases, and adjusting individualized treatments for each patient, thereby contributing to increasing the health and well-being of every individual.

The clinical use of these data poses however novel technical, analytical, ethical and educational challenges to both clinicians and scientists. Clinicians have to learn how to deal with and interpret this new type of data, and society must define where the boundaries of privacy lie. There is a need to bridge the gap between current medical practices and the fruits of technology, namely big data, and to convert them into clinically relevant knowledge. That is the aim of clinical bioinformatics, a very particular application of bioinformatics at the heart of this medical revolution, dedicated to the organization, analysis, interpretation and storage of data pertaining to an individual’s state of health, which can be utilized by medical professionals.

Big data: datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyse.

Personalized medicine, personalized health(care) and precision medicine: These terms refer to the use of combined knowledge (genetic or otherwise) about a person to predict disease susceptibility, disease prognosis, or treatment response and thereby improve that person’s health.