From prehospital care to the emergency department: current status and future directions of rapid EEG and artificial intelligence in the early recognition of status epilepticus

Status epilepticus (SE) represents a time-sensitive, life-threatening neurological emergency. Among its major subtypes, nonconvulsive status epilepticus (NCSE) poses particular diagnostic challenges due to subtle and highly heterogeneous clinical manifestations, frequently resulting in treatment delays and increased risk of adverse outcomes. While conventional electroencephalography (EEG) remains the diagnostic gold standard, timely access to interpretable EEG recordings in emergency department and prehospital settings is often constrained by limited availability of equipment, trained technologists, and neurophysiology expertise. Rapid EEG systems-typically using reduced electrode montages and streamlined application-have emerged to shorten the interval between clinical suspicion and acquisition of actionable EEG data, including point-of-care EEG (POC-EEG). Concurrently, artificial intelligence (AI) has been integrated into EEG analysis platforms to automate detection of epileptiform discharges and quantify seizure burden, thereby mitigating resource constraints associated with real-time interpretation. This narrative review synthesizes technological advances, clinical evidence, and key challenges related to rapid EEG and AI for early recognition of SE/NCSE. Importantly, rapid EEG-whether used alone or with AI-assisted analysis-is designed to augment and support clinical decision-making rather than supplant human expertise. Despite its considerable potential, broad clinical implementation faces challenges related to technical reliability, clinical validation, and ethical concerns.