Role of Artificial Intelligence in Critical Care Medicine: A Literature Review

The growing availability of complex healthcare data, combined with advances in computational methods, has opened new avenues for improving critical care. Intensive care units (ICUs) generate continuous, multimodal data streams, ranging from vital-sign waveforms to laboratory results and clinical notes that place a substantial cognitive burden on clinicians. In recent years, significant focus has emerged on the use of artificial intelligence (AI) in healthcare and the ICU. With an increase in interest and improvement in patient outcomes due to AI use in the ICU, there is a need for an updated summary of current evidence. This review highlights the growing promise of AI in several ICU domains. AI subdomains, machine learning (ML) and deep learning (DL) models, have been shown to accurately predict patient deterioration events such as sepsis, organ failure, and acute respiratory distress syndrome (ARDS) hours in advance. AI-driven image interpretation can enhance diagnostic accuracy in radiology and pathology, and continuous monitoring algorithms can reduce false alarms. In conclusion, AI shows promise for critical care by enabling earlier risk detection, personalized therapy, and optimized resource utilization.