Evidence map›Paper›PMID 40217440›Full record

ArticleActa epileptologica2025

The use of AI in epilepsy and its applications for people with intellectual disabilities: commentary.

Madison Milne-Ives, Rosiered Brownson-Smith, Ananya Ananthakrishnan, Yihan Wang, Cen Cong, Gavin P Winston, Edward Meinert

Abstract readLetter
In one paragraph

Article in Acta epileptologica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Madison Milne-IvesTranslational and Clinical Research Institute, Newcastle University, Newcastle-Upon-Tyne, NE1 7RU, UK.ORCID http://orcid.org/0000-0001-7628-882X
Rosiered Brownson-SmithTranslational and Clinical Research Institute, Newcastle University, Newcastle-Upon-Tyne, NE1 7RU, UK.ORCID http://orcid.org/0000-0002-8960-4761
Ananya AnanthakrishnanTranslational and Clinical Research Institute, Newcastle University, Newcastle-Upon-Tyne, NE1 7RU, UK.ORCID http://orcid.org/0009-0000-0292-3923
Yihan WangTranslational and Clinical Research Institute, Newcastle University, Newcastle-Upon-Tyne, NE1 7RU, UK.ORCID http://orcid.org/0009-0004-1128-6398
Cen CongTranslational and Clinical Research Institute, Newcastle University, Newcastle-Upon-Tyne, NE1 7RU, UK.ORCID http://orcid.org/0009-0007-8261-6480
Gavin P WinstonDepartment of Medicine (Division of Neurology), Queen's University, Kingston, K7L 3N6, Canada.ORCID http://orcid.org/0000-0001-9395-1478
Edward MeinertTranslational and Clinical Research Institute, Newcastle University, Newcastle-Upon-Tyne, NE1 7RU, UK. edward.meinert@newcastle.ac.uk.ORCID http://orcid.org/0000-0003-2484-3347

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial intelligenceEpilepsyIntellectual disabilityPersonalised treatment

Identifiers

PMID40217440
PMCPMC11960292

What Socratic holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.