ReviewCurrent neurology and neuroscience reports2019
Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success.
Review in Current neurology and neuroscience reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.Nursing in critical care · 2026Article
- Development and Validation of a Machine Learning Model for Early Prediction of Acute Kidney Injury in Neurocritical Care: A Comparative Analysis of XGBoost, GBM, and Random Forest Algorithms.Diagnostics (Basel, Switzerland) · 2025Article
- Recent Advances in Flexible Sensors for Neural Interfaces: Multimodal Sensing, Signal Integration, and Closed-Loop Feedback.Biosensors · 2025Review
- Motivating Transparent Communications about Bias in Healthcare Technology Development.Collabra. Psychology · 2025Article
- AI and Neurology.Neurological research and practice · 2025Review
- Artificial Intelligence: Promise or Pitfalls? A Clinical Vignette of Real-Life ChatGPT Implementation in Perioperative Medicine.Journal of general internal medicine · 2024Article
- Current state of neuroprotective therapy using antibiotics in human traumatic brain injury and animal models.BMC neuroscience · 2024Review
- From bed to bench and back again: Challenges facing deployment of intracranial pressure data analysis in clinical environments.Brain & spine · 2024Review
- Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit.BMC neurology · 2023Article
- Research advances in imaging markers for predicting hematoma expansion in intracerebral hemorrhage: a narrative review.Frontiers in neurology · 2023Review
- Predictive modeling in neurocritical care using causal artificial intelligence.World journal of critical care medicine · 2021Review
- Computed Tomography Imaging Predictors of Intracerebral Hemorrhage Expansion.Current neurology and neuroscience reports · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
31720867What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.