Abstract
INTRODUCTION: Epilepsy surgery remains underutilized despite strong evidence supporting its efficacy for appropriately selected patients with drug-resistant epilepsy (DRE).
CASE PRESENTATION: We describe the case of a 23-year-old woman with a 4-year history of refractory focal impaired awareness seizures, experiencing up to four episodes per week despite multiple trialed antiseizure medications at tolerated doses. Initial investigations including magnetic resonance imaging (MRI), EEG, and paraneoplastic screening were unremarkable, and she was managed as having non-lesional left temporal lobe epilepsy. As part of an artificial intelligence driven research initiative, a natural language processing based random forest algorithm reviewing neurology clinic documentation identified her as a potential surgical candidate. Complementary large language model analysis supported extraction of relevant seizure and imaging information. This prompted re-evaluation of the case, with PET demonstrating left temporal hypometabolism and repeat MRI revealing a previously unrecognized encephalocele. She subsequently underwent left temporal lobe polectomy with encephalocele disconnection. The procedure was successful, and she has remained seizure-free for 1 year postoperatively, allowing initiation of ASM weaning and the possibility of long-term cure.
CONCLUSION: This case illustrates the transformative potential of epilepsy surgery for individuals with DRE and the promising role of artificial intelligence augmented triage systems in reducing delays to surgical referral. Given the morbidity, mortality, and economic burden associated with DRE, timely surgical evaluation should be considered the standard of care for eligible patients. Integrating artificial intelligence tools into clinical workflows may help overcome longstanding barriers to access and ensure that life-changing interventions are offered earlier in the disease course.