AI Tool for Early Referral to Epilepsy Surgery

Drug-resistant epilepsy (DRE) affects nearly one-third of people with epilepsy, and among these, patients with focal refractory epilepsy are potential candidates for surgery—the most effective curative treatment. Despite strong evidence that surgery improves seizure outcomes, quality of life, and long-term survival, epilepsy surgery remains significantly underutilized, with patients facing referral delays of 15–20 years. These delays contribute to worsening psychiatric comorbidities, cognitive decline, risk of sudden unexpected death in epilepsy and escalating healthcare costs.

This project addresses the persistent gaps in epilepsy care by developing and piloting a clinician-informed, AI-powered digital health tool to standardize and streamline the identification and referral of surgical candidates. The tool will integrate a validated 73-item questionnaire, expert-labeled patient profiles, and supervised machine learning algorithms to replicate expert assessments of surgical candidacy. Piloting at King Faisal Specialist Hospital & Research Centre (KFSHRC), the national leader in epilepsy surgery, will assess its feasibility, usability, and workflow integration.

The translational impact of this project is substantial by embedding AI decision support into clinical practice, it empowers general neurologists and non-specialist clinicians to recognize surgical candidates earlier, reducing diagnostic and referral delays. Outcomes include improved patient quality of life, reduced morbidity and mortality, and more efficient use of healthcare resources. Beyond epilepsy, this model can serve as a scalable framework for AI-enabled decision support in other areas of neurology and chronic disease management.