The Role of Artificial Intelligence in the Diagnosis and Management of Non-convulsive Seizures: A Narrative Review
Bibliographic record
Abstract
Non-convulsive seizures (NCS) are often underdiagnosed due to their subtle presentation including changes in behavior and mental status. Although electroencephalography (EEG) remains the gold standard for detection, challenges, such as subjective interpretation, individual observer variability, and limited availability, often prolong diagnosis. This can lead to severe complications, including cognitive decline and higher mortality rates. Recent developments in artificial intelligence (AI) are revolutionizing epilepsy care by providing enhanced accuracy and efficiency for diagnosing and managing NCS. Machine learning models, including convolutional neural networks (CNN), recurrent neural networks (RNN), and support vector machines (SVM) have demonstrated high precision in analyzing EEG data and predicting seizures. Innovations such as Ceribell Clarity algorithm (Ceribell, Sunnyvale, CA) allow fast, real-time seizure detection, reducing diagnostic delays in emergency and critical care. Wearable AI-driven technologies like wearable monitoring devices, predictive analytics, and explainable AI enhance personalized care and support better clinician decision-making. This review underlines AI's potential in neurology and neurosurgery, highlighting its role in enhancing diagnostic precision, accelerating interventions, and supporting surgical and treatment planning. By incorporating AI into clinical practice, healthcare systems can overcome diagnostic challenges and deliver patient-centered care. AI is becoming a key element in the future of medicine, driving advances in precision neurology and improving patient outcomes worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".