Physiological Signals Boosted by Artificial Intelligence Save Time and Brain Function

AbstractPatients with acute neurologic injury in the neurologic intensive care unit are at high risk of secondary brain injury, yet timely detection and intervention remain limited by intermittent examination and delayed signal interpretation. This review explores emerging applications of artificial intelligence (AI) and machine learning in neurocritical care, highlighting how continuous physiological data streams - such as electroencephalography, intracranial pressure, and cerebral blood flow - can be used to detect subclinical deterioration and guide early treatment. Through three illustrative cases, we examine potential AI use cases for delayed cerebral ischemia, real-time seizure detection, and continuous physiological "nowcasting" that could improve prognostication and reduce preventable morbidity. Although current AI tools remain largely in developmental or validation stages, their clinical translation will require rigorous testing, thoughtful integration into workflows, and attention to ethical and regulatory considerations. The neurocritical care environment, characterized by rich data and narrow therapeutic windows, represents a uniquely fertile setting for developing clinically actionable AI systems.