Evidence map›Paper›PMID 41443971›Full record

ReviewEpilepsia2026

A call for ethical, equitable, and effective artificial intelligence to improve care for all people with epilepsy: A roadmap. A report by the ILAE Global Advocacy Council and Big Data Commission.

Colin B Josephson, Sandor Beniczky, Spiros Denaxas, Akio Ikeda, Lara Jehi, Angelina Kakooza Mwesige, Nathalie Jette, Gabriel Davis Jones, Philippe Ryvlin, Arjune Sen and 4 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Epilepsia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
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

14 authors.

Colin B JosephsonDepartment of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.ORCID https://orcid.org/0000-0001-7052-1651
Sandor BeniczkyDepartment of Clinical Neurophysiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.ORCID https://orcid.org/0000-0002-6035-6581
Spiros DenaxasInstitute of Health Informatics, University College London, London, UK.
Akio IkedaDepartment of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine Shogoin, Sakyo-ku Kyoto, Japan.ORCID https://orcid.org/0000-0002-0790-2598
Lara JehiEpilepsy Center, Cleveland Clinic, Cleveland, Ohio, USA.ORCID https://orcid.org/0000-0002-8041-6377
Angelina Kakooza MwesigeDepartment of Paediatrics and Child Health, Makerere University College of Health Sciences, Kampala, Uganda.
Nathalie JetteDepartment of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Gabriel Davis JonesOxford Digital Health Labs, Nuffield Department of Women's and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, UK.
Philippe RyvlinDepartment of Clinical Neurosciences, Centre Hospital-Universitaire Vaudois and Université de Lausanne, Lausanne, Switzerland.ORCID https://orcid.org/0000-0001-7775-6576
Arjune SenOxford Epilepsy Research Group, Nuffield Department of Clincial Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK.
Chahnez Charfi TrikiChild Neurology Department, LR19ES15, Sfax Medical School, University of Sfax, Sfax, Tunisia.ORCID https://orcid.org/0000-0003-2918-3819
Gabriella WatersDepartment of Electrical Engineering and Center for Equitable Artificial Intelligence and Machine Learning Systems, Morgan State University, Baltimore, Maryland, USA.
Alla GuekhtMoscow Research and Clinical Center for Neuropsychiatry, Moscow, Russian Federation.
J Helen CrossUCL NIHR BRC Great Ormond Street Institute of Child Health, London, UK.ORCID https://orcid.org/0000-0001-7345-4829

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The artificial intelligence (AI) revolution is upon us. It will inevitably form a central component of epilepsy workflows and patient advocacy. Therefore, it behooves us as health care providers to ride the crest of this wave and guide its direction for the benefit of all people with epilepsy. Emerging AI-based solutions include decision support tools, automated interpretation of electroencephalography (EEG) and brain imaging, and wearable devices that detect seizures and improve patient safety. Pipelines, including decentralized approaches and federated learning, are now being built that will democratize access and facilitate the next generation of AI tools for the global epilepsy community. Despite this, enduring issues remain incompletely addressed. For example, AI requires high volumes of data, leading to concerns about ethical ownership, stewardship, and privacy. Few AI-based tools have progressed from derivation to validation stages, and only rare exceptions undergo real-world evaluation. Inadvertent harmful algorithmic and decision allocation biases also continue to represent major risks to the global epilepsy population. Additional barriers include geographical disparities in computing resources, proprietary ownership of electronic health records, EEG, and brain-imaging platforms, and greenhouse gas emissions related to the demanding power requirements of AI. Therefore, to fully avail ourselves of the benefits of AI, we assert that ethical, equitable, and effective AI for epilepsy requires collaboration from the entirety of the global epilepsy community. Fundamental to this is early and deliberate engagement of people from low- and middle-income countries to ensure that AI-based solutions do not exacerbate existing global disparities. Ultimately, we advocate for "decision intelligence" approaches to the development of AI-based epilepsy solutions, which involves early engagement of all interest-holders to ensure that the correct questions are addressed and the right technical approaches are deployed to maximize value for the global epilepsy community.

Indexed as

Artificial IntelligenceEpilepsyBig DataElectroencephalographyHumansPatient AdvocacyAI ethicscomputational intelligencedata sciencedeep learningIntersectoral Global Action Planmachine intelligencemachine learningsynthetic intelligence

Identifiers

PMID41443971
PMCPMC13075615

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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 OpenQuestion graph.