Systematic Review of the role of Artificial Intelligence and Machine learning in Optimizing Anaesthesia monitoring

Abstract

Artificial intelligence (AI) and machine learning (ML) are transforming the
landscape of anesthesia monitoring, offering unprecedented opportunities to
enhance patient safety, optimize clinical decision-making, and improve
outcomes. This review article provides a comprehensive overview of the
current state of AI and ML techniques in anesthesia monitoring, focusing on
their potential applications, challenges, and future directions. The article
explores how AI and ML can be leveraged to predict adverse events, optimize
anesthetic drug dosing, monitor depth of anesthesia, manage postoperative pain,
and monitor neuromuscular blockade. It also discusses the challenges and
limitations associated with the implementation of AI and ML in anesthesia
monitoring, including data quality and availability, interpretability and
explainability of AI models, ethical considerations, regulatory challenges, and
integration with existing clinical workflows. The future directions for AI and
ML in anesthesia monitoring are outlined, emphasizing the development of
real-time decision support systems, personalized anesthesia care, integration
with other medical devices and systems, and continuous learning and model
adaptation. The article concludes by summarizing the key points, highlighting
the potential impact of AI and ML on anesthesia practice, and calling for further
research and development to address the identified challenges and realize the
full potential of these technologies in anesthesia monitoring.