The use of AI in epilepsy and its applications for people with intellectual disabilities: commentary
Bibliographic record
Abstract
Epilepsy is one of the most common neurological disorders, affecting more than 50 million people worldwide. Management is particularly complex in individuals with intellectual disabilities, who are at a much higher risk of having severe seizures compared to the general population. People with intellectual disabilities are regularly excluded from epilepsy research, despite having significantly higher risks of negative health outcomes and early mortality. Recent advances in artificial intelligence (AI) have shown great potential in improving the diagnosis, monitoring, and management of epilepsy. Machine learning techniques have been used in analysing electroencephalography data for efficient seizure detection and prediction, as well as individualised treatment, which facilitates timely and customised intervention for individuals with epilepsy. Research and implementation of AI-based solutions for people with intellectual disabilities and epilepsy still remains limited due to a lack of accessible long-term clinical data for model training, difficulties in communicating with people with intellectual disabilities, and ethical challenges in ensuring the safety of the AI systems for this population. This paper presents an overview of recent AI applications in epilepsy and for people with intellectual disabilities, highlighting key challenges and the necessity of including people with intellectual disabilities in research on AI and epilepsy, and potential strategies to promote the development and use of AI applications for this vulnerable population. Given the prevalence and consequences associated with epilepsy in people with intellectual disabilities, the application of AI in epilepsy care has the potential to have a significant positive impact. To achieve this impact and to avoid increasing existing health inequity, there is an urgent need for greater inclusion of people with intellectual disabilities in research around the application of AI to epilepsy care and management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.028 | 0.036 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".