Advances in automated anesthesia: a comprehensive review

Abstract

Abstract Anesthesia is a fundamental aspect of modern medical practice, ensuring patient safety and comfort during surgical procedures by effectively managing hypnosis and analgesia. The rapid advancement of artificial intelligence (AI) has facilitated the emergence of automated anesthesia systems, significantly enhancing the precision, efficiency, and adaptability of anesthesia management in complex surgical environments. This review provides a comprehensive survey of the existing literature on automated anesthesia, focusing on three key areas: physiological modeling, automatic anesthesia control, and performance evaluation. It critically examines the strengths and limitations of current methodologies, including traditional statistical learning, machine learning and deep learning approaches, while discussing future development trends in the field. By synthesizing recent technological advancements and clinical applications, this work aims to provide valuable insights for researchers and clinicians, promoting the evolution of intelligent and automated anesthesia practices. Ultimately, this review underscores the transformative potential of AI-driven solutions in delivering personalized anesthesia care, optimizing both hypnosis and analgesia, and enhancing surgical outcomes.

Keywords

• Automatic Anesthesia
• Physiological Modeling
• Closed-loop Anesthesia Control