The integration of artificial intelligence (AI) in anesthesia practices represents a transformative paradigm in the field of medical care. This paper reviews the current applications and explores the future prospects of AI in anesthesia. The current landscape reveals AI's role in optimizing perioperative processes, enhancing patient safety, and improving overall efficiency in anesthesia delivery. Machine learning algorithms, including deep learning models, are being employed to predict patient responses to anesthesia, allowing for personalized and precise administration of anesthesia drugs. Additionally, AI applications contribute to the monitoring of vital signs, real-time data analysis, and early detection of adverse events during surgery. Despite these advancements, challenges such as data privacy, algorithm interpretability, and regulatory considerations need to be addressed. Looking forward, the future holds promising opportunities for AI in anesthesia, including the development of closed-loop systems, continuous monitoring, and the potential for AI-driven decision support systems. As technology continues to evolve, the collaboration between healthcare professionals and AI systems is anticipated to further improve patient outcomes and redefine the landscape of anesthesia administration. This paper underscores the current achievements, challenges, and the immense potential for the continued integration of AI in the practice of anesthesia.

Keywords: Artificial Intelligence (AI), Anesthesia, Machine Learning, Perioperative Optimization, Patient Safety.

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Hussain Marie Alahmari

Specialist in Anesthesia Technology