Artificial Intelligence for Everyone

Gambús, Pedro L.; Shafer, Steven L.

doi:10.1097/aln.0000000000001984

Cited by 38 publications

(19 citation statements)

References 10 publications

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“…As Gambus and Shafer 99 point out in their recent editorial on artificial intelligence, humans have the ability to extrapolate from the known to the unknown based on a more complete understanding of scientific phenomena, but artificial intelligence can only draw conclusions from the data it has seen and analyzed. 99 As more and more elements of clinical practice become digitized and accumulated into databases, we may one day see the development of artificial intelligence systems that have a more complete understanding of clinical phenomena and thus greater potential to deliver elements of anesthesia care autonomously.…”

Section: Artificial Intelligence In Anesthesiologymentioning

confidence: 99%

Artificial Intelligence in Anesthesiology

et al. 2020

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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.

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Section: Artificial Intelligence In Anesthesiologymentioning

confidence: 99%

Artificial Intelligence in Anesthesiology

et al. 2020

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“…26 However, there are areas of concern for the use of ML in anesthesiology and perioperative medicine due to its potential impact on patient safety and reliability, removing the autonomy of clinicians, and negative impact on clinical decision heuristics. [27][28][29][30][31] 1) The very fact that the data used to build the models underlying the mythical ML applications are the drivers of ML read-outs tells us the varying degree of patient safety and reliability of ML. 27 While models are now being refined with increased data points, we cannot deny the future where we interact with these algorithms.…”

Section: Technology Innovations In Anesthesiologymentioning

confidence: 99%

Technology Innovations in Anesthesiology

Schnetz¹,

Kaynar²,

Esper³

et al. 2021

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“…In post hoc analysis, the classical pharmacokinetic/ pharmacodynamic models were able to predict the BIS value with a root-mean-square error of 15 over all phases of the anesthetic. Despite being naive to all existing theory, the neural network comfortably outperformed the best current models with an root-mean-square error of 9-a remarkable victory for modern artificial intelligence over existing classical pharmacokinetic/pharmacodynamic expert systems 47 that might lead us to question the ongoing utility of classical response surface models. 48…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Anesthesiology

Connor

2019

Anesthesiology

151

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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.

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Artificial Intelligence for Everyone

Abstract: "…machine learning to model the interaction of remifentanil and propofol on processed electroencephalogram…. Is this the end of clinical pharmacology?"

Cited by 38 publications

References 10 publications

Artificial Intelligence in Anesthesiology

Artificial Intelligence in Anesthesiology

Technology Innovations in Anesthesiology

Artificial Intelligence and Machine Learning in Anesthesiology

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