Artificial Intelligence for Improving the Management of People With Epilepsy in Low‐and‐Middle Income Countries
Abstract
BACKGROUND: About 80% of people with epilepsy live in low-and-middle-income countries (LMICs) where the treatment gap is high. Limited access to neurologists, diagnostic tools, and antiseizure medications, combined with persistent stigma, contribute to poor outcomes, including premature mortality. Artificial intelligence (AI) offers potential to address these gaps through scalable, low-cost solutions for diagnosis, investigation, management, and monitoring. METHODS: This review examines three recent and complementary AI applications in epilepsy care for LMICs: a smartphone-based diagnostic tool for convulsive epilepsy developed using population-based data from five sub-Saharan African countries; an automated EEG interpretation system based on a deep learning model (SCORE-AI) validated across multicenter datasets; and a wearable-based deep learning model for detecting generalized convulsive seizures using low-cost smartwatches. RESULTS: The smartphone diagnostic tool achieved area under the curve (AUC) 0.92-0.95 with sensitivity 85.0%-97.5% for identifying epilepsy with convulsive seizures using eight binary clinical features. SCORE-AI demonstrated expert-level performance (AUC 0.89-0.96, accuracy 85%-92%) for automated EEG classification across multiple validation datasets. The wearable seizure detection algorithm achieved 96% sensitivity with approximately one false alarm per 8 days. All three solutions were designed for deployment on widely accessible platforms. CONCLUSIONS: AI-driven approaches demonstrate feasibility for addressing diagnostic and monitoring gaps in resource-limited settings. However, implementation faces substantial challenges including infrastructure constraints, limited digital literacy, ethical considerations, and sociocultural factors. Successful deployment requires validation with large locally relevant datasets, context-adapted solutions, task-sharing strategies, implementation research, appropriate regulatory frameworks/certification, and community engagement to reduce the global epilepsy care gap.
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BibTeX
@article{ryvlin2026artificial,
title = {{Artificial Intelligence for Improving the Management of People With Epilepsy in Low‐and‐Middle Income Countries}},
author = {Philippe Ryvlin and Sándor Beniczky and Harald Aurlien and Adriano Bernini and Gabriel Davis Jones and Symon Kariuki and Antoine Spahr and Jesper Tveit and Arjune Sen},
journal = {European Journal of Neurology},
year = {2026},
month = sep,
doi = {10.1111/ene.70740},
url = {https://hal.science/hal-05794734v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial Intelligence for Improving the Management of People With Epilepsy in Low‐and‐Middle Income Countries
AU - Philippe Ryvlin
AU - Sándor Beniczky
AU - Harald Aurlien
AU - Adriano Bernini
AU - Gabriel Davis Jones
AU - Symon Kariuki
AU - Antoine Spahr
AU - Jesper Tveit
AU - Arjune Sen
PY - 2026
DA - 2026/09/08
JO - European Journal of Neurology
DO - 10.1111/ene.70740
UR - https://hal.science/hal-05794734v1
AB - BACKGROUND: About 80% of people with epilepsy live in low-and-middle-income countries (LMICs) where the treatment gap is high. Limited access to neurologists, diagnostic tools, and antiseizure medications, combined with persistent stigma, contribute to poor outcomes, including premature mortality. Artificial intelligence (AI) offers potential to address these gaps through scalable, low-cost solutions for diagnosis, investigation, management, and monitoring. METHODS: This review examines three recent and complementary AI applications in epilepsy care for LMICs: a smartphone-based diagnostic tool for convulsive epilepsy developed using population-based data from five sub-Saharan African countries; an automated EEG interpretation system based on a deep learning model (SCORE-AI) validated across multicenter datasets; and a wearable-based deep learning model for detecting generalized convulsive seizures using low-cost smartwatches. RESULTS: The smartphone diagnostic tool achieved area under the curve (AUC) 0.92-0.95 with sensitivity 85.0%-97.5% for identifying epilepsy with convulsive seizures using eight binary clinical features. SCORE-AI demonstrated expert-level performance (AUC 0.89-0.96, accuracy 85%-92%) for automated EEG classification across multiple validation datasets. The wearable seizure detection algorithm achieved 96% sensitivity with approximately one false alarm per 8 days. All three solutions were designed for deployment on widely accessible platforms. CONCLUSIONS: AI-driven approaches demonstrate feasibility for addressing diagnostic and monitoring gaps in resource-limited settings. However, implementation faces substantial challenges including infrastructure constraints, limited digital literacy, ethical considerations, and sociocultural factors. Successful deployment requires validation with large locally relevant datasets, context-adapted solutions, task-sharing strategies, implementation research, appropriate regulatory frameworks/certification, and community engagement to reduce the global epilepsy care gap.
SN - 1351-5101
SN - 1468-1331
ER -