Every day, more than one thousand people worldwide enter an intensive care unit (ICU) with ARDS, and 35–45% of those with severe illness still die despite guideline-based ventilation and prone positioning.
AI and ML are increasingly being explored as tools that promise to transform this complexity into actionable insight. From early warnings to smarter ventilators, artificial intelligence is helping clinicians outpace ARDS, offering hope for more lives saved through personalized, data-driven care. Convolutional neural networks (CNNs) trained on chest radiographs and ventilator waveforms, as well as gradient boosting models fed raw EHR data, have been shown to achieve area under curve (AUC) values up to 0.95 for detection or prediction tasks in specific settings.
AI and ML can tailor ventilation to individual lung mechanics, and guide costly therapies such as ECMO. Phenotype-aware algorithms already flag patients who benefit from, or suffer from, high PEEP, while neural networks forecast MP-related injury and PVA in real-time. Next-generation GNNs, FL, RL, causal inference, and LLMs may weave disparate data into cohesive bedside recommendations.
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