Artificial intelligence in neurocritical care.

Neurocritical care combines the management of extremely complex disease states with the inherent limitations of clinically assessing patients with brain injury. As the management of neurocritical care patients can be immensely complicated, the automation of data-collection and basic management by artificial intelligence systems have garnered interest. In this opinion article, we highlight the potential artificial intelligence has in monitoring and managing several aspects of neurocritical care, specifically intracranial pressure, seizure monitoring, blood pressure, and ventilation. The two major AI methods of analytical technique currently exist for analyzing critical care data: the model-based method and data driven method. Both of these methods have demonstrated an ability to analyze vast quantities of patient data, and we highlight the ways in which these modalities of artificial intelligence might one day play a role in neurocritical care. While none of these artificial intelligence systems are meant to replace the clinician's judgment, these systems have the potential to reduce healthcare costs and errors or delays in medical management.

Al-Mufti F ，Dodson V ，Lee J ，Wajswol E ，Gandhi C ，Scurlock C ，Cole C ，Lee K ，Mayer SA ... -  《-》

Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success.

Al-Mufti F ，Kim M ，Dodson V ，Sursal T ，Bowers C ，Cole C ，Scurlock C ，Becker C ，Gandhi C ，Mayer SA ... -  《-》

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients.

The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.

Vitt JR ，Mainali S 《-》

The evolution of neurocritical care.

Although neurocritical care as a subspecialty is a relatively young field of medicine, its origins can be traced back to ancient times. This article focuses on the progression of neurocritical care from prehistoric trepanation procedures, through the development of mechanical ventilation, management of increased intracranial pressure, and traumatic brain injury, to the establishment of the first "real" intensive care units, and finally to modern monitoring in neurocritical care, management of post-cardiac arrest patients, and the diagnosis of brain death. This article also focuses on the future direction of neurocritical care.

Korbakis G ，Bleck T 《-》

Influence of data resolution and interpolation method on assessment of secondary brain insults in neurocritical care.

Hemphill JC 3rd ，Barton CW ，Morabito D ，Manley GT ... -  《PHYSIOLOGICAL MEASUREMENT》