Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations

Daniel A Hashimoto, Elan Witkowski, Lei Gao, Ozanan Meireles, Guy Rosman, Daniel A Hashimoto, Elan Witkowski, Lei Gao, Ozanan Meireles, Guy Rosman

Abstract

Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence.The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.

Figures

Fig. 1.
Fig. 1.
Preferred reporting Items for Systematic reviews and Meta-Analyses diagram of screening and evaluation process.
Fig. 2.
Fig. 2.
An illustrative example of a decision node. Several terminologies can be used to describe decision trees. The root node is the start of the tree, and branches connect nodes. A child node is any node that has been split from a previous node, whereas a decision node is any node that allows two or more options to follow it. A chance node is any node that may represent uncertainty.
Fig. 3.
Fig. 3.
An illustrative example of support vector machines. The goal of the support vector machines algorithm is to find the hyperplane that maximizes the separation of features. The solid black line represents the optimal hyperplane, whereas the dotted lines represent the planes running through the support vectors. The empty circle and the solid triangle represent support vectors—the data points from each cluster that represent the closest points to the optimal hyperplane. The dashed line represents the maximum margin between the support vectors.
Fig. 4.
Fig. 4.
An illustrative example of a three-layer neural network. The input layer provides features such as electroencephalogram (EEG) power and entropy, the patient’s mean arterial pressure (MAP), and the patient’s heart rate variability (HrV) to the network. A hidden layer transforms inputs into features usable by the network. The output layer transforms the hidden layer’s activations into an interpretable output (e.g., patient awake vs. asleep).

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