Artificial Intelligence and Machine Learning in Pediatric Epilepsy: A Systematic Review.

Overview

1. Students’ Scientific Society, Pediatric Neurology Department, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
2. Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Zabrze, Poland
3. Department of Histopathology and Cell Pathology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Zabrze, Poland
4. Pediatric Neurology Department, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Medyków 16, 40-752 Katowice, Poland

Abstract

Introduction: To 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.

Methods: Three databases were searched to identify studies in any language that met the eligibility criteria. At least three independent reviewers screened and evaluated each record.

Results: Convolutional 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.

Conclusion: AI 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.

Supplementary Information: The online version contains supplementary material available at 10.1007/s40120-026-00924-0.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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Data

Datasets cited

• doi:10.13026/c2k01r, at the source; found in the references
• physionet.org/content/chbmit, at PhysioNet; found in the references
• zenodo:2547147, at Zenodo; found in the references

Data Availability

All data generated or analyzed during this study are included in this published article or as supplementary information files. Data extracted from studies is available in Tables 2, 3, 4, 5, 6, 7 and Supplementary Material Table S1. Data extracted from the PROBAST-AI questionnaires is available in Supplementary Material Table S2.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 6 keywords, 1 funder, 104 references.

Cite

This paper

Malik, E., Wizner, M., Karpierz, J. I., Pająk, Z., Roszkowska, M., Rojek, M., & Paprocka, J. (2026). Artificial Intelligence and Machine Learning in Pediatric Epilepsy: A Systematic Review. Neurology and therapy, 15(3), 975-1008. https://doi.org/10.1007/s40120-026-00924-0

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