Rapid advances in Artificial Intelligence (AI) have led to diagnostic, therapeutic, and intervention-based applications in the field of medicine. Today, there is a deep chasm between AI-based research articles and their translation to clinical anesthesia, which needs to be addressed. Machine learning (ML), the most widely applied arm of AI in medicine, confers the ability to analyze large volumes of data, find associations, and predict outcomes with ongoing learning by the computer. It involves algorithm creation, testing and analyses with the ability to perform cognitive functions including association between variables, pattern recognition, and prediction of outcomes. AI-supported closed loops have been designed for pharmacological maintenance of anesthesia and hemodynamic management. Mechanical robots can perform dexterity and skill-based tasks such as intubation and regional blocks with precision, whereas clinical-decision support systems in crisis situations may augment the role of the clinician. The possibilities are boundless, yet widespread adoption of AI is still far from the ground reality. Patient-related “Big Data” collection, validation, transfer, and testing are under ethical scrutiny. For this narrative review, we conducted a PubMed search in 2020-21 and retrieved articles related to AI and anesthesia. After careful consideration of the content, we prepared the review to highlight the growing importance of AI in anesthesia. Awareness and understanding of the basics of AI are the first steps to be undertaken by clinicians. In this narrative review, we have discussed salient features of ongoing AI research related to anesthesia and perioperative care.

A study was conducted in an attempt to devise a simple and more accurate method of predicting difficult intubation. Prospective assessments were made in 282 patients and retrospective assessment in 16 patients with regard to 21 anatomical factors which were correlated with the laryngoscopic view at intubation. Twelve factors correlated significantly with difficult intubation. Four of these were eliminated after multifactorial analysis. A scoring system was devised, assigning points to each variable based on its discriminative value. A score of 6 or more correctly identified 22 out of the 23 difficult intubations and there were 50 false positives (sensitivity, specificity and PPV of 96%, 82% and 31% respectively). When negative scoring was done for factors favouring easy intubation, false positives were reduced to 36, but only 20 difficult cases could be identified correctly.

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