Artificial Intelligence in Acute Respiratory Distress Syndrome: Emerging Applications in Diagnosis, Risk Stratification, and Personalized Management
Abstract: Background: Acute respiratory distress syndrome (ARDS) is a heterogeneous, life-threatening form of acute respiratory failure associated with substantial morbidity and mortality. Despite advances in lung-protective ventilation, prone positioning, and supportive critical care, early recognition, accurate phenotyping, individualized ventilatory management, and prognostic assessment remain challenging. Artificial intelligence (AI), particularly machine learning (ML) and deep learning, offers novel approaches for integrating complex clinical, physiological, imaging, waveform, and biomarker data. Objective: Our review examines current and emerging applications of AI in ARDS, with emphasis on diagnosis, early prediction, phenotyping, risk stratification, ventilation optimization, and prognosis. Review: AI-based models can analyze electronic health records, laboratory parameters, vital-sign trajectories, chest imaging, ventilator waveforms, and molecular biomarkers to identify patients at risk of ARDS before conventional clinical recognition. ML-based phenotyping may distinguish biologically and clinically meaningful subgroups, including hyperinflammatory and hypoinflammatory phenotypes. AI-assisted ventilation may facilitate individualized selection of tidal volume, positive end-expiratory pressure, inspired oxygen concentration, and detection of patient?ventilator asynchrony. Machine-learning models also demonstrate promising performance for mortality prediction and longitudinal risk assessment. However, limitations include retrospective development, dataset bias, inadequate external validation, limited interpretability, and uncertain clinical effectiveness. Conclusion: AI may transform ARDS management from syndrome-based supportive care toward dynamic, data-driven precision critical care. Prospective multicentre validation and clinically integrated trials remain essential before widespread implementation.
Keywords: Artificial intelligence; Acute respiratory distress syndrome; Machine learning
How to Cite?: Dr. Tanmayee Siddharth Gajhas, Dr. Narendrasinh Solanki, Dr. Abhinav Lambe, Dr. Pranav Deshmukh, "Artificial Intelligence in Acute Respiratory Distress Syndrome: Emerging Applications in Diagnosis, Risk Stratification, and Personalized Management", Volume 15 Issue 9, September 2026, International Journal of Science and Research (IJSR), Pages: 1558-1562, https://www.ijsr.net/getabstract.php?paperid=SR26924234659, DOI: https://dx.doi.org/10.21275/SR26924234659