Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography

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.