BACKGROUND

Patient-ventilator asynchronies (PVAs) are frequent complications in mechanically ventilated patients, contributing to adverse outcomes such as ventilator-induced lung injury, prolonged mechanical ventilation, and increased mortality. Artificial intelligence (AI) has emerged as a promising tool for enhancing PVA detection, prediction, and optimization. Despite its growing potential, the full scope of AI applications in this field and persistent gaps in evidence remain inadequately explored.

OBJECTIVE

This scoping review examines the breadth of AI-based approaches for managing PVAs, identifying key methodologies, evaluating research trends, and highlighting limitations in the current literature.

METHODS

A comprehensive search was conducted in PubMed, Embase, Science Direct, IEEE Xplore, and the Cochrane Library without time restrictions. Extracted data included study objectives, AI methodologies, patient populations, performance metrics, and clinical outcomes. The findings were synthesized into thematic categories to map advancements and research gaps.

RESULTS

Twenty-six studies were identified that applied AI techniques to detect, predict, or optimize PVAs. The included studies employed a range of AI methodologies, including convolutional neural networks, long short-term memory networks, and hybrid algorithms. These models demonstrated high predictive performance, with accuracy ranging from 87 % to 99 % and AUROC values exceeding 0.98 for detecting complex asynchronous events. AI systems also showed promise in predicting weaning success and optimizing ventilatory settings through real-time patient-specific adjustments. However, challenges such as reliance on single-center datasets, inconsistencies in calibration, and limited implementation of explainable AI frameworks restrict their clinical applicability.

CONCLUSIONS

AI holds transformative potential in managing PVAs by enabling real-time detection, improved weaning prediction, and personalized ventilatory strategies. However, significant challenges remain, particularly the need for multi-center validation, standardized reporting protocols, and randomized controlled trials to evaluate clinical efficacy. Addressing these gaps is essential for integrating AI into routine critical care practice and transitioning from theoretical models to practical, real-world applications in intensive care units.