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

Daniel A. Hashimoto, M.D., M.S.; Elan Witkowski, M.D., M.P.H.; Lei Gao, M.D.; Ozanan Meireles, M.D.; Guy Rosman, Ph.D.

Disclosures

Anesthesiology. 2020;132(2):379-394. 

In This Article

Abstract and Introduction

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.

Introduction

Artificial intelligence has been defined as the study of algorithms that give machines the ability to reason and perform functions such as problem-solving, object and word recognition, inference of world states, and decision-making.[1] Although artificial intelligence is often thought of as relating exclusively to computers or robots, its roots are found across multiple fields, including philosophy, psychology, linguistics, and statistics. Thus, artificial intelligence can look back to visionaries across those fields, such as Charles Babbage, Alan Turing, Claude Shannon, Richard Bellman, and Marvin Minsky, who helped to provide the foundation for many of the modern elements of artificial intelligence.[2] Furthermore, major advances in computer science, such as hardware-based improvements in processing and storage, have enabled the base technologies required for the advent of artificial intelligence.

Artificial intelligence has been applied to various aspects of medicine, ranging from largely diagnostic applications in radiology[3] and pathology[4] to more therapeutic and interventional applications in cardiology[5] and surgery.[6] In April 2018, the U.S. Food and Drug Administration approved the first software system that uses artificial intelligence—a program that assists in the diagnosis of diabetic retinopathy through the analysis of images of the fundus.[7] As the development and application of artificial intelligence technologies in medicine continues to grow, it is important for clinicians in every field to understand what these technologies are and how they can be leveraged to deliver safer, more efficient, more cost-effective care.

Anesthesiology as a field is well positioned to potentially benefit from advances in artificial intelligence as it touches on multiple elements of clinical care, including perioperative and intensive care, pain management, and drug delivery and discovery. We conducted a scoping review of the literature at the intersection of artificial intelligence and anesthesia with the goal of identifying techniques from the field of artificial intelligence that are being used in anesthesia research and their applications to the clinical practice of anesthesiology.

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