ReviewEpilepsia open2025
Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges.
Review in Epilepsia open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Preclinical translational epilepsy research uses animal models to better understand the mechanisms underlying epilepsy and its comorbidities, as well as to analyze and develop potential treatments that may mitigate this neurological disorder and its associated conditions. Artificial intelligence (AI) has emerged as a transformative tool across various fields, including neuroscience research. AI can assist in the acquisition and analysis of data throughout the experimental process. Currently, the integration of AI techniques, including machine learning (ML), assumes an important role in preclinical epilepsy research. For analytical purposes, the techniques described in this review are categorized into three principal domains based on their objectives. Diagnosis involves identification, characterization, and/or prediction of epileptic seizures utilizing experimental data such as EEG recordings. Identification of comorbidities associated with epilepsy using AI represents a significant advancement in preclinical research. This approach can lead to a comprehensive understanding of the interplay between epilepsy and related disorders. The treatment domain involves the utilization of ML models to conduct simulations and computational analyses to comprehend the underlying mechanisms of epilepsy, discern potential drug targets, and evaluate the efficacy of experimental medications, thereby facilitating the translation of discoveries into clinical settings. This paper aimed to present, explain, and scrutinize some of the AI techniques used in recent years within preclinical epilepsy research. Moreover, advantages, challenges, ethical considerations, reporting issues, and future perspectives will be discussed. PLAIN LANGUAGE SUMMARY: Researchers study epilepsy using animal models to understand its mechanisms and develop novel therapeutic strategies. Artificial intelligence (AI) is becoming an important tool in this work, helping with data collection and analysis. In this critical review, AI techniques are grouped into three main areas: diagnosis of seizures, identification of health disorders associated with epilepsy, and exploration of new treatments. AI enables scientists to spot patterns in brain activity, find connections between epilepsy and other conditions, and test potential medications. This review also examines the advantages, challenges, and future of using AI in this field.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.