ReviewNeurology and therapy2026
Artificial Intelligence and Machine Learning in Pediatric Epilepsy: A Systematic Review.
Review in Neurology and therapy, 2026. 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.
- Timely Surgical Approaches for Pediatric Epilepsy Resistant to Medication.Journal of surgery and research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
introductionTo evaluate the progress of artificial intelligence (AI)-based tools in interpreting clinical data, to compare the existing models, to identify the most proficient models and to determine the limitations of current models and existing research.
methodsThree databases were searched to identify studies in any language that met the eligibility criteria. At least three independent reviewers screened and evaluated each record.
resultsConvolutional neural networks emerged as the predominant deep learning architecture, whereas support vector machines were the most frequently used classical machine learning approach. AI-driven seizure detection showed diagnostic performance approaching that of experienced specialists. Reported applications include the detection of specific epilepsy syndromes, identification of electrical status epilepticus during sleep (ESES) and spike-wave index, continuous electroencephalography (EEG) surveillance, neonatal monitoring, and wearable seizure detection technologies.
conclusionAI models achieved diagnostic accuracy exceeding 90% in distinguishing normal EEG recordings from abnormal ones, with automated seizure detection representing the most widespread case of clinical use. Despite encouraging results, the reliability of these systems is constrained by limited cohort sizes (typically < 100 patients). Future efforts should focus on large-scale, multicenter validation to enable clinical implementation. PROTOCOL REGISTRATION: INPLASY database, https://doi.org/10.37766/inplasy2025.10.0020 .
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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 OpenQuestion graph.