Authors
Zack Goldblum, Haoer Shi, Zhongchuan Xu, William K S Ojemann, Carlos A Aguila, Kevin Long, Kevin Xie, Kerry C Nix, Katherine G Walsh, Ellie Chang, Sarah B Lavelle, Brandon Bach, Kathryn A Davis, Nishant Sinha, Lauren H Hammer, Erin C Conrad, Brian Litt
Published in
Nature medicine. Oct 13, 2026. Epub Oct 13, 2026.
Abstract
One-third of the world's 70 million people with epilepsy have seizures uncontrolled by medication, and implantable devices are a promising treatment. These devices can detect impending seizures, missed medication and impaired cognition, but, like most medical devices, they cannot share this information with patients in real time. This is a missed opportunity for devices to learn from clinical events and teach patients how their behaviors affect their health. Here we present a device platform that converses with patients to help them manage their epilepsy. The inpatient prototype links scalp and intracranial electroencephalography (EEG) to a secure, large language model-based artificial intelligence (AI) that communicates bidirectionally through a smartphone app. The AI processes biomarkers of sleep, medication, cognition and seizure probability, notifies patients of clinical events and trends, records symptoms and responses, and uses their feedback to retrain its seizure detector, reducing false alarms. We validated this platform in 13 patients who are undergoing video-EEG monitoring for epilepsy (scalp, n = 11; intracranial, n = 2). In a pseudoprospective simulation, seizure detectors improved precision from patient feedback alone, without expert intervention. We propose this system as a scalable, safeguarded platform for medical devices that can rapidly adapt to patient and provider needs across epilepsy and other medical conditions.
PMID:
42869994
Bibliographic data and abstract were imported from PubMed on 14 Oct 2026.
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