Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success
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Purpose of Review
Neurocritical care combines the complexity of both medical and surgical disease states with the inherent limitations of assessing patients with neurologic injury. Artificial intelligence (AI) has garnered interest in the basic management of these complicated patients as data collection becomes increasingly automated.
In this opinion article, we highlight the potential AI has in aiding the clinician in several aspects of neurocritical care, particularly in monitoring and managing intracranial pressure, seizures, hemodynamics, and ventilation. The model-based method and data-driven method are currently the two major AI methods for analyzing critical care data. Both are able to analyze the vast quantities of patient data that are accumulated in the neurocritical care unit.
AI has the potential to reduce healthcare costs, minimize delays in patient management, and reduce medical errors. However, these systems are an aid to, not a replacement for, the clinician’s judgment.
KeywordsMultimodality monitoring Artificial intelligence Neurocritical care Closed-loop system
The editors would like to thank Dr. John Brust for taking the time to review this manuscript.
Compliance with Ethical Standards
Conflict of Interest
Fawaz Al-Mufti, Michael Kim, Vincent Dodson, Tolga Sursal, Christian Bowers, Chad Cole, Corey Scurlock, Christian Becker, Chirag Gandhi, and Stephan A. Mayer each declare no potential conflicts of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
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