ORIGINAL RESEARCH article
Front. Neurol.
Sec. Artificial Intelligence in Neurology
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1571398
Research status and trends of artificial intelligence applications in epilepsy: A bibliometric analysis
Provisionally accepted
- 1Shandong Second Medical University, Weifang, Shandong Province, China
- 2Jinan Central Hospital, Shandong University, Jinan, China
- 3Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China
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Objective: To explore the research status and emerging directions in the application of artificial intelligence (AI) in epilepsy.Methods: Scientific papers on AI applications for epilepsy from January 1, 1970, to December 31, 2024, were retrieved from the Web of Science Core Collection (WoSCC), Scopus, and PubMed. Only articles and reviews in English were included in this study. A manual review was performed to exclude irrelevant studies. Bibliometric analysis and visualization maps were generated using CiteSpace, VOSviewer, and R package.Results: In total, 5,155 relevant papers were retrieved from the WoSCC, Scopus, and PubMed databases. The United States led in terms of centrality (0.23) and citations (35399). According to Bradford's law, 17 core journals dominated this field, with Epilepsia publishing the most papers (n =208). Acharya UR emerged as the most influential author, with an h-index of 24 and a g-index of 30. Among the 6206 author keywords, 83 appeared more than 24 times. Keyword co-occurrence analysis identified six distinct clusters, among which clusters 1 and 3 comprised keywords with the latest average publication year. Citation burst detection indicated that keywords such as "ensemble learning," "seizure type classification," "unsupervised learning," and "anomaly detection" started emerging in 2021, 2022, 2022, and 2022, respectively, with this trend continuing. The network map generated by VOSviewer included these four keywords in clusters 1 and 3.Conclusions: This was the first bibliometric analysis of AI applications in epilepsy. Over the past five years, the number of publications on this topic has grown substantially, which is expected to continue. The field is dominated by 17 core journals. The journal Epilepsia and the author Acharya UR were the most influential journal and author, respectively. Key research areas moving forward include "brain modeling for seizure prediction on the Internet of Things" and "deep learning for detecting interictal epileptiform discharge."
Keywords: artificial intelligence, Epilepsy, application, bibliometric analysis, deep learning
Received: 05 Feb 2025; Accepted: 29 Apr 2025.
Copyright: © 2025 Wu, Qi, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Guangxin Wang, Jinan Central Hospital, Shandong University, Jinan, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.