• Jonathan Montomoli, Matthias P Hilty, and Can Ince.
• Department of Anesthesia and Intensive Care, Infermi Hospital, AUSL Romagna, Rimini, Italy.
• Minerva Anestesiol. 2022 Dec 1; 88 (12): 106610721066-1072.

AbstractThe high complexity of care in the Intensive Care Unit environment has led, in the last decades, to a big effort in term of the improvement of patient's monitoring devices, increase of diagnostic and therapeutic opportunities, and development of electronic health records. Such advancements have enabled an increasing availability of large amounts of data that were supposed to provide more insight and understanding regarding pathophysiological processes and patient's prognosis providing useful tools able to support physicians in the clinical decision-making process. On the contrary, the interpolation, analysis, and interpretation of a such big amount of data has soon proven to be much more complicated than expected, opening the way for the development of tools based on machine learning (ML) algorithms. However, at the present, most of the AI-based algorithms developed in intensive care do not reach beyond the prototyping and development environment and are still far from being able to assist physicians at the bedside in the clinical decisions to improve quality and efficiency of care. The present review aimed to provide an overview of the status of ML-based algorithms in intensive care, to explore the concept of digital transformation, and to highlight possible next steps necessary to move towards a routine use of ML-based clinical decision support systems at the bedside. Finally, we described our attempt to apply the pillars of digital transformation in the field of microcirculation monitoring with the creation of the Microcirculation Network Research Group (MNRG).

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