Evaluating and Validating an Artificial Intelligence Model for Automated Electroencephalogram Analysis: Implications for Clinical Practice

Epilepsy affects around 50 million people worldwide and remains a major diagnostic challenge, particularly in resource-limited settings. Electroencephalography (EEG) is essential for diagnosis but relies heavily on expert interpretation, often limited by workforce shortages. Artificial intelligence (AI) offers a promising solution to automate EEG interpretation, enhance diagnostic accuracy, and improving diagnostic efficiency. This retrospective diagnostic validation study was conducted to evaluate the performance of an AI-based system for automated EEG interpretation. A total of 649 EEG recordings from patients aged 1–91 years were analyzed, with expert neurophysiologist interpretations serving as the reference standard. The AI model, developed using a deep learning architecture, was trained to classify EEGs as normal or abnormal and to further categorize findings into epileptiform-focal, epileptiform-generalized, non-epileptiform-focal, and non-epileptiform-diffuse. Performance metrics included sensitivity, specificity, accuracy, area under the ROC curve (AUC), and Cohen’s kappa coefficient for agreement. The model achieved an overall diagnostic accuracy of 93.8% (95% CI: 90.9–96.0) and an AUC of 0.94, demonstrating strong discriminative ability. Sensitivity for abnormal EEG detection was 99.0%, with specificity of 89.7%, PPV of 98.7%, and NPV of 90.0%. Agreement with expert interpretations was κ = 0.87 (p < 0.001), indicating almost perfect concordance. The model maintained robust performance across clinical contexts, with false positives (5.5%) exceeding false negatives (0.5%), reflecting a safety-oriented error profile suited for screening. No statistically significant impact of artifact presence, sleep state, or EEG type was observed on classification accuracy. The model demonstrated high diagnostic accuracy and near-perfect agreement with expert interpreters, highlighting its potential as a clinical decision-support tool for EEG triage and preliminary screening. Integration into real-world workflows could help alleviate workforce shortages, reduce diagnostic delays, and improve early epilepsy detection—particularly in underserved regions. Further refinement, including enhanced artifact handling and diverse dataset validation, will be essential for clinical deployment.