Evidence map›Paper›PMID 40426652›Full record

ReviewBrain sciences2025

Assistive Artificial Intelligence in Epilepsy and Its Impact on Epilepsy Care in Low- and Middle-Income Countries.

Nabin Koirala, Shishir Raj Adhikari, Mukesh Adhikari, Taruna Yadav, Abdul Rauf Anwar, Dumitru Ciolac, Bibhusan Shrestha, Ishan Adhikari, Bishesh Khanal, Muthuraman Muthuraman

Abstract readReview
In one paragraph

Review in Brain sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. 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

10 authors.

Nabin KoiralaSchool of Medicine, Yale University, New Haven, CT 06511, USA.ORCID 0000-0002-8261-8271
Shishir Raj AdhikariNepal Applied Mathematics and Informatics Institute for Research, Kathmandu 44700, Nepal.
Mukesh AdhikariGilling's School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA.
Taruna YadavSchool of Medicine, Yale University, New Haven, CT 06511, USA.
Abdul Rauf AnwarInstitut du Cerveau-Paris Brain Institute, 75013 Paris, France.ORCID 0000-0001-5352-4615
Dumitru CiolacDepartment of Neurology, State University of Medicine and Pharmacy "Nicolae Testemitanu", MD-2004 Chisinau, Moldova.ORCID 0000-0003-1243-313X
Bibhusan ShresthaDepartment of Surgery, Kathmandu University Hospital, Dhulikhel 45200, Nepal.
Ishan AdhikariDepartment of Neurology, University of Texas, San Antonio, TX 78249, USA.
Bishesh KhanalNepal Applied Mathematics and Informatics Institute for Research, Kathmandu 44700, Nepal.
Muthuraman MuthuramanNeural Engineering with Signal Analytics and Artificial Intelligence, Department of Neurology, University of Wurzburg, 97070 Wurzburg, Germany.ORCID 0000-0001-6158-2663

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epilepsy, one of the most common neurological diseases in the world, affects around 50 million people, with a notably disproportionate prevalence in individuals residing in low- and middle-income countries (LMICs). Alarmingly, over 80% of annual epilepsy-related fatalities occur within LMICs. The burden of the disease assessed using Disability Adjusted Life Years (DALYs) shows that epilepsy accounts for about 13 million DALYs per year, with LMICs bearing most of this burden due to the disproportionately high diagnostic and treatment gaps. Furthermore, LMICs also endure a significant financial burden, with the cost of epilepsy reaching up to 0.5% of the Gross National Product (GNP) in some cases. Difficulties in the appropriate diagnosis and treatment are complicated by the lack of trained medical specialists. Therefore, in these conditions, adopting artificial intelligence (AI)-based solutions may improve epilepsy care in LMICs. In this theoretical and critical review, we focus on epilepsy and its management in LMICs, as well as on the employment of AI technologies to aid epilepsy care in LMICs. We begin with a general introduction of epilepsy and present basic diagnostic and treatment approaches. We then explore the socioeconomic impact, treatment gaps, and efforts made to mitigate these issues. Taking this step further, we examine recent AI-related developments and their potential as assistive tools in clinical application in LMICs, along with proposals for future directions. We conclude by suggesting the need for scalable, low-cost AI solutions that align with the local infrastructure, policy and community engagement to improve epilepsy care in LMICs.

Indexed as

artificial intelligenceepilepsyepilepsy careepilepsy eiagnosislow and middle income countries

Identifiers

PMID40426652
PMCPMC12110662

What Socratic holds

Textmetadata
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.