ROLE OF ARTIFICIAL INTELLIGENCE IN NEURO-ICU: FROM MONITORING TO PROGNOSIS
Keywords:
Neuro-intensive care unit, artificial intelligence, machine learning, patient monitoring, neuroimaging, prognosis, outcome prediction, EEG, intracranial pressure, deep learningAbstract
Advances in artificial intelligence (AI) and machine learning are rapidly transforming neuro-intensive care units (Neuro-ICUs), enhancing patient monitoring, diagnostics and prognostication. This review explores the integration and application of AI-driven technologies within Neuro-ICUs, highlighting their role in real-time multimodal monitoring, early detection of neurological complications and accurate outcome prediction. AI-based algorithms utilizing continuous EEG, intracranial pressure, cerebral oxygenation and neuroimaging data offer significant improvements in detecting vasospasm, seizures and cerebral edema, facilitating timely interventions. Predictive modeling through deep learning and neural networks has shown promise in forecasting long-term outcomes, such as Glasgow Outcome Scale–Extended (GOS-E) and modified Rankin Scale (mRS) scores, thereby aiding clinical decision-making and family counseling. Despite these advances, significant challenges remain, including data privacy concerns, interpretability of algorithms, and clinical integration. This article synthesizes recent literature (2018–2025) to evaluate the potential, limitations, and ethical implications of AI applications in Neuro-ICUs and provides insights into future research directions aimed at optimizing patient care and outcomes.
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