Document Type
Article
Publication Date
9-3-2026
Abstract
Electroencephalography is the most commonly used diagnostic tool for epilepsy. However, interpreting electroencephalograms (EEGs) requires expertise that is not widely available. Advances in digital technology and wearables have enabled large-scale EEG recording, generating vast amounts of data that cannot be managed through traditional visual interpretation by experts. Artificial intelligence (AI) has the potential to augment human expertise and reduce workloads. The application of artificial neural networks in analysing clinical EEG recordings has led to major breakthroughs, bringing AI-based EEG interpretation closer to clinical implementation. In this Review, we summarise the most important research and development results in this field from a clinical perspective. We provide an overview of AI applications in spike and seizure detection; analysis of data from wearable electroencephalographs, patients who are critically ill, and epilepsy surgery; and the automated interpretation of clinical EEGs.
Recommended Citation
Beniczky, Sándor; Frauscher, Birgit; Nascimento, Fábio; Sivathamboo, Shobi; Rojas, Catalina; Sperling, Michael; Lhatoo, Samden; Ryvlin, Philippe; and Kuhlmann, Levin, "Artificial Intelligence Models for Automated and Semiautomated Analysis and Interpretation of Clinical Electroencephalography" (2026). Department of Neurology Faculty Papers. Paper 415.
https://jdc.jefferson.edu/neurologyfp/415
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
42692953
Language
English
Comments
This article is the author’s final published version in The Lancet Digital Health, Volume 8, Issue 9, 2026, Article number 101023.
The published version is available at https://doi.org/10.1016/j.landig.2026.101023. Copyright © 2026 The Author(s).