Artificial intelligence-integrated electroencephalography (AI-EEG) has demonstrated promise in the early detection of nonconvulsive status epilepticus (NCSE), particularly in emergency and intensive care settings with limited access to trained EEG technologists. This review includes 20 studies, of which 12 were incorporated into a meta-analysis assessing the diagnostic accuracy of AI-EEG. The pooled sensitivity reached 95%, with a specificity of 83%. However, when the pretest probability of NCSE is 40%, false positives may occur in approximately one in seven patients. Commercial AI-EEG platforms have shown a reduction in unnecessary antiepileptic drug (AED) administration compared to clinical judgment alone. Four prospective cohort studies reported a 26% relative risk reduction (RR -0.26; 95% CI -0.50 to -0.02; p = .03) in unnecessary AED use. Additionally, AI-EEG shortened the median time to EEG acquisition in resource-limited settings-from 4.5 hours (IQR 3.2-6.8) to 2.1 hours (IQR 1.5-3.4). A sub-analysis from an industry-sponsored trial suggested potential benefits of AI-EEG in reducing morbidity and ICU length of stay, though evidence remains insufficient for definitive conclusions. Despite these advantages, rapid-deployment AI-EEG systems face challenges: lack of video integration makes it difficult to distinguish seizures from artifacts or behavioral events, and limited electrode coverage may miss central brain activity. Moreover, AI algorithms tend to overread sharp and spike activities compared to human interpretation. Further investigator-initiated studies are needed to evaluate the diagnostic yield of AI-EEG beyond its simplified setup, assess its true impact on patient outcomes, and determine its feasibility for large-scale clinical implementation. .
Gomutbutra et al. (2025) studied this question.