Artificial intelligence and anesthesia

Abstract

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.

Recent advances in system management, decision support systems, artificial intelligence and computing in anaesthesia

“…It is the most common and highly advanced part of AI in anaesthesiology. [ 7 ] Opal is the first documented example of a full-stack platforms infrastructure designed for ML in anaesthesia, setting the way for a variety of potential clinical applications in areas such as data mining, medical simulation, high-frequency predictions and quality improvement. [ 3 ]…”

“…As a result, it would be beneficial if automated decision support for anaesthetic monitoring was made available, and this is where AI, CDSS, ML and block-chain technology (BCT) play an important role. [ 5 – 7 ]…”

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“…The extensive knowledge of physiology, pathology, pharmacology and, of course, the clinic that an intensivist has, together with the wisdom and manual skills of a good anesthesiologist, are the ideal couple both in the operating room, in post-surgical recovery, and in the intensive care unit. [1][2][3][4][5][6] Having simultaneously practiced anesthesiology and critical medicine during four decades has been a unique professional pleasure of its kind, a complete satisfaction that I enjoyed every moment of my professional life, and that as a very special achievement led me to be the chief editor of the Journal of Anesthesia and Critical Care:Open Access, which has given me the unique opportunity to read ahead of time the unpublished ideas of colleagues from diverse regions of our planet; from sites with unlimited resources to places where the available technology is scarce.…”

Closed-Loop Controlled Fluid Administration Systems: A Comprehensive Scoping Review

“…The first scenario, where these systems can be safely operated, will probably be the operating room, where constant supervision by an anesthesiologist provides an important safety net; Deeper understanding of fluid dynamics and their translation to ever-more-complex computational models, meant for better accuracy and validity of both controllers and in silico testing platforms [ 18 , 79 , 80 ]; Introduction of new modalities of artificial intelligence, such as reinforcement learning [ 81 , 82 ] and other deep-learning modalities. While there’s increasing use of deep-learning for anesthesia and critical care-related applications [ 83 , 84 ], we have not identified detailed reports on deep-learning-based systems matching our inclusion criteria, meaning we have not identified a system that incorporates deep-learning-based capabilities into a CL system (or, for that matter, a DS system with a feedback loop of repeat evaluations); Continuing formation of a regulatory pipeline dedicated to autonomous and semi-autonomous controlled systems; Increased use of non-invasive sensors in closed-loop fluid administration systems, as their reliability will gradually increase [ 43 , 85 ], as well as artificial intelligence-based advanced sensing modalities (specifically, feature extraction), such as arterial waveform feature analysis [ 77 , 78 ], aimed at providing personalized resuscitation goals; Gradual increase in the degree of automation—from a regulatory standpoint, decision support systems are generally considered safer and easier to approve. However, they do not offer the same mental offloading and adherence as a closed-loop or even a provider-in-loop system can potentially offer.…”

“…Introduction of new modalities of artificial intelligence, such as reinforcement learning [81,82] and other deep-learning modalities. While there's increasing use of deeplearning for anesthesia and critical care-related applications [83,84], we have not identified detailed reports on deep-learning-based systems matching our inclusion criteria, meaning we have not identified a system that incorporates deep-learning-based capabilities into a CL system (or, for that matter, a DS system with a feedback loop of repeat evaluations); • Continuing formation of a regulatory pipeline dedicated to autonomous and semiautonomous controlled systems;…”