Artificial Intelligence for Mechanical Ventilation: A Transformative Shift in Critical Care.

With the large volume of data coming from implemented technologies and monitoring systems, intensive care units (ICUs) represent a key area for artificial intelligence (AI) application. Despite the last decade has been marked by studies focused on the use of AI in medicine, its application in mechanical ventilation management is still limited. Optimizing mechanical ventilation is a complex and high-stake intervention, which requires a deep understanding of respiratory pathophysiology. Therefore, this complex task might be supported by AI and machine learning. Most of the studies already published involve the use of AI to predict outcomes for mechanically ventilated patients, including the need for intubation, the respiratory complications, and the weaning readiness and success. In conclusion, the application of AI for the management of mechanical ventilation is still at an early stage and requires a cautious and much less enthusiastic approach. Future research should be focused on AI progressive introduction in the everyday management of mechanically ventilated patients, with the aim to explore the great potentiality of this tool. Plain Language Summary: The use of artificial intelligence for mechanical ventilation Artificial Intelligence (AI) could help the management of patients treated with mechanical ventilation in critical care practice. Current guidelines are based on data coming from the general population, without considering the individual patients' characteristics. With the use of AI for mechanical ventilation, critical care practice could be improved by offering personalized treatments, reducing complications, and assisting clinicians in decision-making to improve patient outcomes and reduce mortality rates. Despite AI in medicine has progressed in the last decade, little is known about its use in critical care and in ventilation management. In order to improve its everyday use, future research should be performed in intensive care settings. [ABSTRACT FROM AUTHOR]