Artificial Intelligence and Machine Learning in Anesthesiology

Christopher W. Connor, M.D., Ph.D.

Disclosures

Anesthesiology. 2019;131(12/31/1899):1346-1359. 

In This Article

Abstract and Introduction

Abstract

Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning. Progress had historically languished in these areas, such that these skills had come to seem ineffably bound to intelligence. However, these commercial advances have performed best at single-task applications in which imperfect outputs and occasional frank errors can be tolerated.

The practice of anesthesiology is different. It embodies a requirement for high reliability, and a pressured cycle of interpretation, physical action, and response rather than any single cognitive act. This review covers the basics of what is meant by artificial intelligence and machine learning for the practicing anesthesiologist, describing how decision-making behaviors can emerge from simple equations. Relevant clinical questions are introduced to illustrate how machine learning might help solve them—perhaps bringing anesthesiology into an era of machine-assisted discovery.

Introduction

The human mind excels at estimating the motion and interaction of objects in the physical world, at inferring cause and effect from a limited number of examples, and at extrapolating those examples to determine plans of action to cover previously unencountered circumstances. This ability to reason is backed by an extraordinary memory that subconsciously sifts events into those experiences that are pertinent and those that are not, and is also capable of persisting those memories even in the face of significant physical damage. The associative nature of memory means that the aspects of past experiences that are most pertinent to the current circumstance can be almost effortlessly recalled to conscious thought. However, set against these remarkable cerebral talents are fatigability, a cognitive laziness that presents as a tendency to short-cut mental work, and a detailed short-term working memory that is tiny in scope. The human mind is slow and error-prone at performing even straightforward arithmetic or logical reasoning.[1]

In contrast, an unremarkable desktop computer in 2019 can rapidly retrieve and process data from 32 gigabytes of internal memory—a quarter of a trillion discrete bits of information—with absolute fidelity and tirelessness, given an appropriately constructed program to execute. The greatest progress in artificial intelligence has historically been in those realms that can most easily be represented by the manipulation of logic and that can be rigorously defined and structured, known as classical or symbolic artificial intelligence. Such problems are quite unlike the vagaries of the interactions of objects in the physical world. Computers are not good at coming to decisions—indeed, the formal definition of the modern computer arose from the proof that certain propositions are logically undecidable[2]—and classical approaches to artificial intelligence do not easily capture the idea of a "good enough" solution.

For most of human history, the practice of medicine has been predominantly heuristic and anecdotal. Traditionally, quantitative patient data would be relatively sparse, decision making would be based on clinical impression, and outcomes would be difficult to relate with much certainty to the quality of the decisions made. The transition to evidence-based practice[3] and Big Data is a relatively recent occurrence. In contrast, anesthesiologists have long relied on personalized streams of quantified data to care for their unconscious patients, and advances in monitoring and the richness of that data have underpinned the dramatic improvements in patient safety in the specialty.[4] Anesthesiologists also practice at the sharper end of cause and effect: decisions usually cannot be postponed, and errors in judgment are often promptly and starkly apparent.

The general question of artificial intelligence and machine learning in anesthesiology can be stated as follows:

  1. There is some outcome that should be either attained or avoided.

  2. It is not certain what factors lead to that outcome, or a clinical test that predicts that outcome cannot be designed.

  3. Nevertheless, a body of patient data is available that provides at least circumstantial evidence as to whether the outcome will occur. The data are plausibly, but not definitively, related.

  4. The signal, if it is present in the patient data, is too diffuse across the data set for it to be learned reliably from the number of cases that an anesthesiologist might personally encounter, or the clinical decision-making relies upon a subconscious judgment that the anesthesiologist cannot elucidate.

  5. Can an algorithm, derived from the given data and outcomes, provide insight in order to improve patient management and the decision-making process?

This form of machine learning might be termed machine-assisted discovery.

This article takes the form of an integrative review,[5] defined as "a review method that summarizes past empirical or theoretical literature to provide a more comprehensive understanding of a particular phenomenon or healthcare problem." The article therefore introduces the theory underlying classical and modern approaches to artificial intelligence and machine learning, and surveys current empirical and clinical areas to which these techniques are being applied. Concepts in the fundamentals of artificial intelligence and machine learning are introduced incrementally:

  1. Beginning with classical or symbolic artificial intelligence, a logical representation of the problem is crafted and then searched for an optimal solution.

  2. Model fitting of physiologic parameters to an established physiologic model is shown as an extension of search.

  3. Augmented linear regression is shown to allow certain nonlinear relationships between outcomes and physiologic variables to be discerned, even in the absence of a defined physiologic model. It requires sufficient expertise about which combinations of nonphysiologic transformations of the variables might be informative.

  4. Neural networks are shown to provide a mechanism to establish a relationship between input variables and an output without defining a logical representation of the problem or defining transformations of the inputs in advance. However, this flexibility comes at considerable computational cost and a final model with a behavior that may be hard to comprehend.

Numerous other theoretical and computational approaches do exist, and these may have practical advantages depending on the nature of the problem and the structure of the desired outputs.[6]

The literature search for an integrative review should be transparent and reproducible, comprehensive but focused and purposive. A literature search was performed using PubMed for articles published since 2000 using the following terms: "artificial intelligence anesthesiology" (543 matches), "computerized analysis anesthesiology" (353 matches), "machine learning anesthesiology" (91 matches), and "convolutional neural network anesthesiology" (1 match). Matches were reviewed for suitability, and augmented with references of historical significance. The specialty of anesthesiology features a broad history of attempts to apply computational algorithms, artificial intelligence, and machine learning to tasks in an attempt to improve patient safety and anesthesia outcomes (Table 1). Recent significant and informative empirical advances are reviewed more closely.

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