MODERN STRATEGIES IN THE TREATMENT OF ACUTE RESPIRATORY DISTRESS SYNDROME (ARDS): FROM VENTILATORY SUPPORT TO ARTIFICIAL INTELLIGENCE-BASED MONITORING SYSTEMS

Background and objective: Acute respiratory distress syndrome (ARDS) remains one of the leading causes of mortality among patients treated in intensive care units. Despite the use of lung-protective ventilation, prone positioning, and rescue therapies, mortality in severe cases continues to exceed 30–40%. In recent years, the precise optimization of ventilator settings has gained particular importance, including the control of driving pressure, mechanical power, and patient–ventilator asynchrony, while simultaneously the use of advanced monitoring systems and artificial intelligence (AI)–based decision-support tools have expanded rapidly. The aim of this review is to present contemporary, advanced methods of ARDS management, ranging from complex mechanical ventilation strategies to AI-driven solutions, with particular emphasis on their effectiveness, safety, and potential for clinical application. Scope of review: This review includes literature from 2020–2025 addressing interventional and technological aspects of ARDS treatment, with a specific focus on lung-protective ventilation and mechanical power regulation, the application of prone positioning, strategies for reducing patient–ventilator asynchrony, the use of ECMO as a rescue therapy, and AI models designed for outcome prediction, assessment of readiness for ventilator liberation, and automated asynchrony detection. The analysis primarily incorporates observational and cohort studies, methodological work, and publications dedicated to integrating AI into intensive care unit practice and ventilator systems. Findings: Modern mechanical ventilation strategies concentrate on minimizing ventilator-induced lung injury (VILI) by reducing tidal volumes, limiting driving pressure, and decreasing mechanical power. At the same time, accumulating evidence suggests that clinically subtle patient–ventilator asynchrony may worsen outcomes, creating opportunities for machine-learning algorithms capable of its automatic identification. AI models are also being applied to predict weaning success, assess the risk of extubation failure, and develop decision-support systems that integrate ventilator parameters, vital signs, and laboratory data. Although preliminary findings are promising, most AI algorithms remain in developmental stages and require further validation. Conclusions: Current therapeutic approaches in ARDS are shifting away from the exclusive use of lung-protective ventilation principles toward highly personalized strategies incorporating mechanical power assessment, asynchrony analysis, and AI-based predictive tools. The synergistic integration of advanced ventilatory support with AI systems may enable more precise tailoring of therapy to ARDS phenotypes, improve decision-making regarding ventilation management and liberation, and potentially enhance clinical outcomes, however, large, well-designed prospective and randomized studies are needed to determine the impact of these innovations on mortality, ventilator-free days, and the safety of applied interventions.