Artificial Intelligence supported ventilator management: Current applications, clinical evidence, and future directions

Abstract

Background Mechanical ventilation remains a complex intervention requiring continuous adjustment to balance oxygenation, ventilation, lung protection, patient–ventilator synchrony, and readiness for liberation. Artificial intelligence (AI) may support more individualized ventilator management by integrating multidimensional intensive care unit data. Methods We conducted a targeted narrative review of PubMed and Embase for studies evaluating AI, machine learning, deep learning, reinforcement learning, and closed-loop systems in ventilator management. Findings AI applications in ventilator management include outcome prediction, patient–ventilator asynchrony detection, ventilator setting optimization, and closed-loop ventilation. Current studies show promising performance in predicting extubation and weaning outcomes, detecting asynchrony from ventilator waveforms, forecasting high risk ventilation patterns, and supporting automated ventilator adjustments. However, most evidence remains retrospective, singlecenter, simulation based, or focused on model performance rather than patient centered outcomes. Conclusions AI-supported ventilator management is a rapidly evolving field with potential to enhance individualized respiratory care. Future studies should prioritize external validation, prospective trials, workflow integration, explainability, and safety frameworks to determine whether AI can improve clinically meaningful outcomes in mechanically ventilated patients. Keywords: artificial intelligence, mechanical ventilation, ventilator management, patient–ventilator asynchrony, closed-loop ventilation