• Fawaz Al-Mufti, Michael Kim, Vincent Dodson, Tolga Sursal, Christian Bowers, Chad Cole, Corey Scurlock, Christian Becker, Chirag Gandhi, and Stephan A Mayer.
• Departments of Neurosurgery, Westchester Medical Center at New York Medical College, Valhalla, NY, USA. fawazalmufti@outlook.com.
• Curr Neurol Neurosci Rep. 2019 Nov 13; 19 (11): 89.

Purpose Of ReviewNeurocritical 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.Recent FindingsIn 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.

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