Artificial intelligence (AI) and machine learning are poised to significantly improve the management of acute respiratory distress syndrome (ARDS). AI facilitates earlier detection of ARDS, more accurate risk stratification, and the potential for personalised treatment plans. By analysing complex patient data, AI can assist clinicians in making quicker, more informed decisions in the ICU.
AI applications in ARDS range from predicting and diagnosing the condition using diverse data sources to optimising mechanical ventilation and supporting ECMO decisions. AI algorithms can identify ARDS subtypes, predict patient responses to specific interventions, and ultimately facilitate a move towards precision medicine.
Despite the promise of AI in reshaping ARDS care, challenges remain in ensuring data quality, model generalisability, and seamless clinical integration. Further real-world validation is needed to fully realise the potential of AI-driven strategies in improving patient outcomes.
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