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@article{180252,
        author = {Mr Mahesh Subhash Thange and Miss Saranya Saravanan and Ms Priyanka Suryakant Deorankar and Miss Sumedha Prakash Bane and Mr Vaibhav Pandurang Chopade and Mr Amit Sureshrao Sontakke},
        title = {Artificial Intelligence in General Anaesthesia: Applications in Monitoring, Prediction, and Automation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {913-920},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180252},
        abstract = {General anaesthesia (GA) is a cornerstone of modern surgical practice, enabling pain-free and controlled procedures by inducing reversible unconsciousness and loss of sensation. The complexity and dynamic nature of GA management demand continuous monitoring, timely decision-making, and precise drug administration to ensure patient safety. In recent years, artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering advanced capabilities in data analysis, pattern recognition, and real-time decision support.
This review explores the integration of AI technologies into the domain of general anaesthesia, focusing on three key applications: monitoring, prediction, and automation. In monitoring, AI enables real-time analysis of physiological parameters and depth of anaesthesia, enhancing intraoperative safety. In predictive modelling, AI algorithms assist in anticipating adverse events such as hypotension, awareness during anaesthesia, and post-operative complications, thereby allowing for proactive interventions. In automation, AI-driven closed-loop systems and decision-support tools contribute to optimized drug delivery and anaesthesia management with minimal human input.
Overall, the integration of AI into anaesthesia practice holds the potential to improve precision, safety, and efficiency. This paper aims to provide a comprehensive overview of current advancements, practical applications, and future directions of AI in general anaesthesia.},
        keywords = {Artificial intelligence, machine learning, general anaesthesia, monitoring, prediction, automation, closed-loop systems, EEG, risk assessment, clinical decision support, robotics, patient safety, perioperative care.},
        month = {June},
        }