Editorial: Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume I

Abstract

in Emergency and Critical Care Medicine, Volume I Analytics based on artificial intelligence (AI) has greatly advanced a variety of scientific research fields such as natural language processing, imaging classification and signal processing (1). Clinical research is also revolutionized by the development of artificial intelligence (2), and conventional research paradigm is being supplemented by the new technology. Conventional treatment strategy based on evidence-based medicine typically exploits the average treatment effect in a population to dictate medical decision making (3). However, it is well-known that a patient population is usually heterogeneous that one size does not fit all. In other words, although a treatment strategy is reported to be beneficial for the overall population, it might be harmful for a subgroup of patients. Thus, the idea of individualized treatment is proposed to address the problem of differential treatment effects in a heterogeneous population. Patients in emergency and critical care setting are usually heterogeneous and the clinical condition changes rapidly (4, 5), which highlights the importance of early risk stratification and individualized treatment.

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