patient risk and help in anesthetic management planning, optimization, and patient counseling.Large perioperative datasets from several sites are being used more and more to stratify risk using machine learning, which is particularly helpful when handling difficult cases with uncommon comorbidities. 5 Predictive algorithms can classify patients based on their likelihood of experiencing perioperative complications, and AI has been implemented in airway assessment, accurately predicting the Cormack-Lehane classification through facial and neck analysis. 6 Preoperative checklists and clinical decision support systems for antibiotic selection and dosing have also been tested at various centers. 7 Intraoperative monitoring: The integration of AI algorithms with monitoring systems enables real-time notifications for aberrations in vital signs, helping anesthesiologists in the early detection of adverse events such as hypotension or arrhythmias. 8,9AI reduces IntroductIonArtificial intelligence (AI) replicates human cognitive functions, facilitating machines' performance of tasks that traditionally entail human intelligence, such as decision-making, problem-solving, and learning. 1Anesthesiology has a longstanding tradition of innovation, from advanced monitoring systems to breakthroughs in pharmacology.Recently, AI has emerged as a pivotal force in the specialty, enhancing clinical decision-making, streamlining workflows, and strengthening patient safety. 2 Notably, large language models, such as ChatGPT, exhibit substantial potential in healthcare applications.This narrative review adheres to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and examines AI's role, particularly that of ChatGPT, in routine anesthesia practices by evaluating its benefits, challenges, and future prospects.A systematic literature search was conducted on MEDLINE/PubMed/EMBASE using key terms such as "Artificial intelligence in anesthesia," "ChatGPT applications in anesthesia," and "AI and ChatGPT benefits in anesthesia."The available literature was critically analyzed, and the manuscript was written afterwards. Artificial Intelligence in AnesthesiaArtificial intelligence encompasses a wide range of technologies, including machine learning (ML), natural language processing (NLP), and deep learning, which analyze extensive datasets, identify patterns, and provide predictive insights. 3 It is redefining the medical practice by improving diagnostic accuracy, aiding in personalizing treatment, streamlining administrative tasks, and also enhancing patient outcomes, and hence, better quality of life. 4 In anesthesiology, AI applications in anesthesiology are numerous, including: Preoperative assessment: By analyzing electronic health records to find comorbidities, AI-driven systems evaluate
Depuru et al. (Tue,) studied this question.