Clinical management of sepsis can be improved by artificial intelligence: yes

The management of sepsis is a highly complex, multifaceted challenge that remains the realm of highly skilled and trained human experts. But as medical applications of artificial intelligence continue to pour in, it is becoming obvious that some of these decisions could soon be left to machines that could be dubbed "intelligent", improving clinical practice and patient outcomes [1]. Indeed, most of the tasks involved in the clinical management of sepsis (early recognition, selection of antibiotic therapy, haemodynamic optimisation, etc.) could be individually performed or optimised by dedicated algorithms. Most of what we call "artificial intelligence" is in fact machine learning--a set of computer tools intended to generate new knowledge from data [1]. Machine learning includes three categories of techniques: supervised (which uses labelled data to build a prediction model, for example for prognostication), unsupervised (which discovers patterns in data and generates clusters of subjects that share common characteristics) and reinforcement learning (where a sequential decision process is modelled and optimised). Below, I have selected a few significant applications that I consider the most likely to land in the clinical environment in the near future, either because of their robustness or their potential.