Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know: More than Black Magic for the Art of the Dark

As demonstrated by the studies in this issue, the principal advantage of machine learning is the boost in performance it achieves when attempting to predict an observed outcome for which the range of explanatory features is large, or the depth of interactions between features is overwhelmingly complex. To predict hypotension, Hatib et al.⁵  brilliantly tap into vast arrays of data within the arterial line waveform, extending far beyond simple characteristics such as heart rate and blood pressure (and furthermore, far beyond “complex” characteristics such as pulse pressure variation, systolic pressure rise [dP/dt], and waveform area). When posed with an analytic task in which potential predictive features are in the thousands or millions, or of nuanced complexity, the flexibility of machine-learning techniques to accommodate inputs simply outmatch any traditional analytic method. In biomedical literature, other fields leveraging machine learning to tackle complex tasks include image processing (e.g., computer vision) of radiographic⁹  or whole-slide pathology¹⁰  images, as well as text analysis (natural language processing) of clinical notes.^11,12 

In contrast, for predictive analytic tasks in which features remain countable, or relationships explainable, machine learning may still prove useful, but will likely be of more modest benefit. In the article by Kendale et al.,⁴  an ensemble of machine-learning methods indeed outperformed a classic logistic regression approach for predicting hypotension, but the overall performance of the machine-learning model remained far from perfect. In the case of the best-performing algorithm (gradient boosting machines), Kendale et al.⁴  demonstrate a relatively small improvement compared to a classic logistic regression approach. Similarly, whereas Lee et al.³  successfully demonstrate a deep learning approach to predicting postoperative mortality from intraoperative data, the authors fail to demonstrate improvement compared to logistic regression, a recurring issue in studies promoting the use of deep learning.

As with all methodologic approaches, machine learning is not without drawbacks. The most hotly contested is the difficulty of understanding mechanisms driving the prediction models presented. Herein lies the “black magic” of machine learning: although the predictive performance of a machine-learning algorithm can be precisely quantified—and sometimes, this performance is staggering—the question of how to interpret and act upon the information generated remains wholly unanswered. In cases where mechanisms are of limited concern, or penalties for incorrect predictions low—such as facial recognition in family photos—machine-learning techniques deftly succeed in their purpose. Conversely, in cases where mechanisms are critical, and penalties for error are high—as is often the case in health care, and particularly in anesthesiology—a machine-learning approach falling anywhere short of nearly perfect remains unviable. Hatib et al.⁵  importantly note that although prediction of hypotension can be established with high fidelity, it remains entirely unclear as to how a clinician should respond to such an alert. This issue is even more critical, considering the generalizability and reproducibility concerns of such models. In many studies leveraging machine learning, insufficient testing and validation of complex models, particularly those using deep learning, can lead to overfitting of even the largest of datasets.

Despite such limitations, the articles in this issue^3–5  take courageous shifts in methodologic approaches, and unmistakably establish that machine-learning applications to anesthesiology are not just a fad. The authors should be commended as exemplars for assertively applying new scientific paradigms to our field. How such machine-learning techniques are harnessed in order to improve anesthesia, and more broadly advance health sciences, remains a challenge for decades to come.

All work and partial funding attributed to the Department of Anesthesiology, University of Michigan Medical School (Ann Arbor, Michigan). This editorial review described was supported in part by the National Heart, Lung, and Blood Institute of the National Institutes of Health (Bethesda, Maryland), grant No. 1K01HL141701-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.