Artificial intelligence-assisted categorization of interictal abnormalities in adult long-term electroencephalographic monitoring: Comparable diagnostic accuracy with marked efficiency gain

Daniel Mansilla, Jesper Tveit, Harald Aurlien, Tamir Avigdor, Chafaou Abdallah, Pilar Bosque-Varela,

Melita Cacic Hribljan, Victoria Ros-Castelló, Donald Schomer, Jean Gotman, Sandor Beniczky

Epilepsia

2026

Abstract

Artificial intelligence (AI) classifies routine electroencephalograms (EEGs) with expert-level accuracy, but interpretation of long-term EEG monitoring (LTM) remains reliant on time-intensive visual review. We evaluated whether autoSCORE, a clinically approved implementation of SCORE-AI integrated into an EEG reader, achieves expert-level diagnostic accuracy for LTM interpretation and whether expert-supervised use reduces review time versus conventional analysis.