<![CDATA[Artificial Intelligence (AI) is rapidly evolving and has become a transformative innovation in anesthesiology and intensive care. Technologies such as machine learning, deep learning, clinical decision support systems (CDSS), and Generative Artificial Intelligence (GenAI) offer significant potential to enhance clinical practice. This literature review discusses recent developments, clinical applications, implementation challenges, and future directions of AI in anesthesiology and intensive care units (ICU). Studies indicate that AI can enhance preoperative assessment through more accurate risk stratification, predict intraoperative hypotension, optimize fluid therapy and hemodynamic management, assist with monitoring anesthesia depth, and support ultrasound-guided regional anesthesia. In intensive care units, AI contributes to early sepsis detection, prediction of acute kidney injury, optimization of mechanical ventilation, identification of multiorgan failure risk, and mortality prediction. The emergence of GenAI and Large Language Models (LLMs) has further expanded AI applications in medical education, literature reviews, research, clinical documentation, and guideline development. However, AI implementation continues to face challenges, including limited prospective validation, algorithmic bias, insufficient explainability, the risk of LLM hallucinations, data security concerns, and ethical and medicolegal issues.Artificial Intelligence (AI) is not intended to replace anesthesiologists, but it serves as augmented intelligence that supports evidence-based clinical decision-making. Responsible AI implementation requires strengthening digital infrastructure, developing algorithms based on local data, and increasing literacy. Artificial Intelligence (AI) for health workers, as well as regulations that ensure security, transparency, and accountability. With this approach, AI has the potential to improve the quality, safety, efficiency, and personalization of anesthesiology services and intensive care.]]>
