ReviewJournal of clinical medicine2025
Advancements and Challenges of Artificial Intelligence-Assisted Electroencephalography in Epilepsy Management.
Review in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Lightweight deep learning model for nonconvulsive status epilepticus diagnosis using EEG time-frequency analysis.Scientific reports · 2026Article
- Point-of-Care EEG for Non-Convulsive Seizure and Status Epilepticus: Advances, Limitations, and Future Directions.Journal of clinical medicine · 2026Review
- Early Prediction of Delirium in Postcardiac Surgery Patients: Machine Learning Model Development and External Validation.JMIR medical informatics · 2026Article
- Generalization or mirage? Data leakage and reported performance in neonatal EEG seizure detection models: a systematic review.BioData mining · 2026Review
- Modeling multiscale neural dynamics for EEG-based emotion recognition using an attentive wavelet-transformer framework.Frontiers in computational neuroscience · 2026Article
- Research on epilepsy detection methods based on interpretable features and machine learning.PloS one · 2026Article
- Artificial Intelligence-Driven Neuromodulation in Neurodegenerative Disease: Precision in Chaos, Learning in Loss.Biomedicines · 2025Review
- From prehospital care to the emergency department: current status and future directions of rapid EEG and artificial intelligence in the early recognition of status epilepticus.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Artificial intelligence (AI) has emerged as a transformative tool in the analysis and management of epilepsy through its integration with electroencephalography (EEG) data. The adoption of AI-assisted solutions in managing epilepsy holds the potential to significantly enhance the efficiency and accuracy for diagnosing this complex condition. However, AI-assisted EEG technologies are infrequently adopted in clinical settings. In this Review, we provide an overview of AI applications in seizure prediction, detection, syndrome classification, surgical planning, and prognosis prediction. Additionally, we explore the methodological considerations and challenges that are relevant in clinical settings. Overall, AI has the potential to revolutionize epilepsy management, ultimately improving patient outcomes and advancing the field of precision medicine. Fostering interdisciplinary collaborations between AI researchers, neurologists, and ethicists will be crucial in creating integrated solutions that address both technical and clinical requirements.
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.