Fig. 1

The circular bytes-to-bedside approach to data science and artificial intelligence: the care and treatment of critically ill patients generates large amounts of data that are routinely stored in electronic health records. These data may be leveraged to develop predictive models using artificial intelligence techniques. These models may be deployed as software that interacts with the electronic health record providing computerized decision support to intensive care professionals to improve care and treatment at the bedside of critically ill patients. The five pillars from the ESICM data science section strategy aim to support this circular bytes-to-bedside approach: providing data science education for intensive care professionals, promoting of data science research in intensive care medicine, providing guidance on responsible data sharing, benchmarking and standard setting for electronic health records in intensive care units and maintaining a framework for collaboration in the field of data science and intensive care medicine