Artificial intelligence and machine learning in acute respiratory distress syndrome management: recent advances

Acute Respiratory Distress Syndrome (ARDS) remains a critical challenge in intensive care, marked by high mortality and significant patient heterogeneity, which limits the effectiveness of conventional supportive therapies. This review highlights the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in revolutionizing ARDS management. We explore diverse AI/ML applications, including early prediction and diagnosis using multi-modal data (electronic health records [EHR], imaging, ventilator waveforms), advanced prognostic assessment and risk stratification that outperform traditional scoring systems, and precise identification of ARDS subtypes to guide personalized treatment. Furthermore, we detail AI's role in optimizing mechanical ventilation (e.g., PEEP settings, patient-ventilator asynchrony detection, mechanical power-guided strategies), facilitating Extracorporeal Membrane Oxygenation (ECMO) support decisions, and advancing drug discovery. The review also delves into cutting-edge methodologies such as Graph Neural Networks, Causal Inference, Federated Learning, Self-Supervised Learning, and the emerging paradigm of Large Language Models (LLMs) and agent-based AI, which promise enhanced data integration, privacy-preserving research, and autonomous decision support. Despite challenges in data quality, model generalizability, interpretability, and clinical integration, AI-driven strategies offer unprecedented opportunities for precision medicine, real-time decision support, and ultimately, improved patient outcomes in ARDS.