The Role of Artificial Intelligence in the Diagnosis and Management of Non-convulsive Seizures: A Narrative Review

Non-convulsive seizures (NCS) are often underdiagnosed due to their subtle presentation including changes in behavior and mental status. Although electroencephalography (EEG) remains the gold standard for detection, challenges, such as subjective interpretation, individual observer variability, and limited availability, often prolong diagnosis. This can lead to severe complications, including cognitive decline and higher mortality rates. Recent developments in artificial intelligence (AI) are revolutionizing epilepsy care by providing enhanced accuracy and efficiency for diagnosing and managing NCS. Machine learning models, including convolutional neural networks (CNN), recurrent neural networks (RNN), and support vector machines (SVM) have demonstrated high precision in analyzing EEG data and predicting seizures. Innovations such as Ceribell Clarity algorithm (Ceribell, Sunnyvale, CA) allow fast, real-time seizure detection, reducing diagnostic delays in emergency and critical care. Wearable AI-driven technologies like wearable monitoring devices, predictive analytics, and explainable AI enhance personalized care and support better clinician decision-making. This review underlines AI's potential in neurology and neurosurgery, highlighting its role in enhancing diagnostic precision, accelerating interventions, and supporting surgical and treatment planning. By incorporating AI into clinical practice, healthcare systems can overcome diagnostic challenges and deliver patient-centered care. AI is becoming a key element in the future of medicine, driving advances in precision neurology and improving patient outcomes worldwide.