The intensive care unit (ICU) represents one of the most data-rich and clinically complex environments in modern medicine. Artificial intelligence (AI) has emerged as a transformative force in critical care, offering the potential to enhance clinical decision-making, predict adverse events, and improve patient safety. This review provides a comprehensive examination of AI applications in intensive care, focusing on three core domains: predictive analytics for early detection of clinical deterioration, clinical decision support systems for diagnosis and treatment optimization, and patient safety through adverse event prediction and prevention. We synthesize evidence from recent studies demonstrating that AI models can predict sepsis, acute kidney injury, and cardiac arrest with AUC values of 0.85–0.94, often hours before clinical recognition. Clinical.
decision support systems have shown promise in ventilator management, fluid resuscitation, and antibiotic stewardship, though their impact on patient outcomes remains variable. Patient safety applications, including fall prediction, medication error detection, and pressure injury risk assessment, have demonstrated accuracy improvements of 15–30% over traditional risk scores. Despite these advances, significant barriers to clinical adoption persist, including concerns about algorithmic bias, explainability, integration into clinical workflows, and the lack of prospective validation. We conclude that while AI holds substantial promise for transforming intensive care, successful implementation requires rigorous prospective validation, seamless workflow integration, and a commitment to addressing ethical and practical challenges. The future of AI in critical care lies not in replacing clinical judgment but in augmenting it, providing clinicians with timely, accurate, and actionable insights to improve patient outcomes.
Keywords: Artificial Intelligence, Intensive Care Unit, Predictive Analytics, Clinical Decision Support, Patient Safety, Machine Learning, Critical Care.
Citation: Chadwick, F. (2026). Artificial Intelligence in Intensive Care: A Comprehensive Review of Predictive Analytics, Clinical Decision Support, and Patient Safety. J Prim Health Glob Health 1(1):1-9.