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CC BY 4.0 · J Neuroanaesth Crit Care
DOI: 10.1055/s-0044-1787844
Review Article

The Promise of Artificial Intelligence in Neuroanesthesia: An Update

Zhenrui Liao
1   Department of Neuroscience, Columbia University, New York, New York, United States
,
Niharika Mathur
2   School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, Georgia, United States
,
Vidur Joshi
3   Department of Biomedical Engineering, Steven's Institute of Technology, Hoboken, New Jersey, United States
,
Shailendra Joshi
4   Department of Anesthesiology, Columbia University, New York, New York, United States
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Abstract

Artificial intelligence (AI) is poised to transform health care across medical specialties. Although the application of AI to neuroanesthesiology is just emerging, it will undoubtedly affect neuroanesthesiologists in foreseeable and unforeseeable ways, with potential roles in preoperative patient assessment, airway assessment, predicting intraoperative complications, and monitoring and interpreting vital signs. It will advance the diagnosis and treatment of neurological diseases due to improved risk identification, data integration, early diagnosis, image analysis, and pharmacological and surgical robotic assistance. Beyond direct medical care, AI could also automate many routine administrative tasks in health care, assist with teaching and training, and profoundly impact neuroscience research. This article introduces AI and its various approaches from a neuroanesthesiology perspective. A basic understanding of the computational underpinnings, advantages, limitations, and ethical implications is necessary for using AI tools in clinical practice and research. The update summarizes recent reports of AI applications relevant to neuroanesthesiology. Providing a holistic view of AI applications, this review shows how AI could usher in a new era in the specialty, significantly improving patient care and advancing neuroanesthesiology research.

Keywords

neuroanesthesiology - anesthesiology - artificial intelligence - deep learning - machine learning


Publication History

Article published online:
06 August 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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