Fawaz Al-Mufti1,2,3, Michael Kim4, Vincent Dodson5, Tolga Sursal4, Christian Bowers4, Chad Cole4, Corey Scurlock6,7, Christian Becker6,8, Chirag Gandhi4, Stephan A Mayer9. 1. Departments of Neurosurgery, Westchester Medical Center at New York Medical College, Valhalla, NY, USA. fawazalmufti@outlook.com. 2. Departments of Neurology, Westchester Medical Center at New York Medical College, Valhalla, NY, USA. fawazalmufti@outlook.com. 3. Neuroendovascular Surgery and Neurocritical Care Attending, Westchester Medical Center at New York Medical College, 100 Woods Road, Macy Pavilion 1331, Valhalla, NY, 10595, USA. fawazalmufti@outlook.com. 4. Departments of Neurosurgery, Westchester Medical Center at New York Medical College, Valhalla, NY, USA. 5. Department of Neurosurgery, New Jersey Medical School, Rutgers University, Newark, NJ, USA. 6. eHealth Center, Westchester Medical Center Health Network, Valhalla, NY, USA. 7. Departments of Anesthesiology, Westchester Medical Center at New York Medical College, Valhalla, NY, USA. 8. Departments of Internal Medicine, Westchester Medical Center at New York Medical College, Valhalla, NY, USA. 9. Department of Neurology, Henry Ford Health System, Detroit, MI, USA.
Abstract
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. RECENT FINDINGS: 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.
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. RECENT FINDINGS: 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.
Entities:
Keywords:
Artificial intelligence; Closed-loop system; Multimodality monitoring; Neurocritical care
Authors: Craig M Lilly; Marc T Zubrow; Kenneth M Kempner; H Neal Reynolds; Sanjay Subramanian; Evert A Eriksson; Crystal L Jenkins; Teresa A Rincon; Benjamin A Kohl; Robert H Groves; Elizabeth R Cowboy; Kamana E Mbekeani; Mark J McDonald; Dominick A Rascona; Michael H Ries; Herbert J Rogove; Ahmed E Badr; Isabelle C Kopec Journal: Crit Care Med Date: 2014-11 Impact factor: 7.598