Artificial Intelligence in Intensive Care Units: Clinical Applications, Ethical Challenges and Future Directions - A Narrative Review

Artificial intelligence (AI) is increasingly recognized as a transformative tool in intensive care units (ICUs), where large volumes of complex patient data present both challenges and opportunities for clinical decision-making. This narrative review aims to synthesize current evidence on the applications of AI in ICU settings, focusing on predictive analytics, clinical decision support systems, therapeutic optimization, and ethical and implementation challenges. A targeted literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar, prioritizing peer-reviewed studies published between 2015 and 2025. Findings were synthesized thematically. The results indicate that AI models can outperform traditional clinical scoring systems in predicting sepsis, mortality, and clinical deterioration. Reinforcement learning approaches further demonstrate potential for personalized treatment strategies. However, significant barriers to implementation remain, including limited external validation, lack of interpretability, ethical concerns, and infrastructural limitations. AI holds substantial promise, but its successful integration requires careful consideration of clinical, ethical, and organizational factors. Future research should focus on explainable AI, prospective validation, and seamless integration into clinical workflows.