ReviewDigital health
A comprehensive evaluation of interpretable artificial intelligence for epileptic seizure diagnosis using an electroencephalogram: A systematic review.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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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.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- Clinical application of artificial intelligence technology in epilepsy.Acta epileptologica · 2026Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Epilepsy is a sensitive social and health issue that causes sudden death in epilepsy. Awake and sleep electroencephalogram (EEG) first test confirms 80% of patients with confirmed epilepsy. Explainable artificial intelligence (XAI) for epileptic seizures (ESs) emerged to overcome drawbacks of artificial intelligence (AI) models like lack of right to explain, fairness, and trustworthiness, and an overwhelming paper was published. However, there is a lack of reporting interpretable and performance tradeoffs, stating the most interpretable AI applied, describing the most useful waveforms learned in XAI models, documenting areas of interest, and identifying the relationship between frequency bands and epilepsy. Therefore, this systematic review aims to comprehensively evaluate the performance and the interpretability of interpretable AI methods used for ES monitoring using an EEG. Methods: This study followed PRISMA guidelines for systematic review. Advanced search queries were hardheaded into five reputable databases. Rayyan online platform for a systematic review was used. The disagreement was resolved through discussions. Results: Twenty-three papers are included. A total of 14 datasets are used. A total of 16,200 populations are participated in all the included studies. CHB-MIT Dataset is frequently used (12 times). Minimizing the number of waveforms learned will increase the accuracy and reduce the memory used. Interpretability to accuracy trade-offs are observed in the studies included. Discussion: The result of this systematic review implies that further studies are needed on interpretable to accuracy tradeoffs, multi-modal care recommendations, and onset early warning to minimize sudden unexpected death in epilepsy and damage. Optimizing waveforms for ESs needs more investigation. Subjective matrices must be investigated very well before being used by XAI. This study has no ethical considerations associated with it. It has been registered with PROSPERO: registration number: CRD42023479926.
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