Multimodal artificial intelligence in epilepsy: antiseizure medication response, epileptogenic zone localization, and surgical outcome prediction.
Authors
Affiliations (5)
Affiliations (5)
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
- School of Public Health, Suzhou Medical College of Soochow University, Suzhou, China.
- International Lab for Child Medical Imaging Research, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
Abstract
Epilepsy remains difficult to manage because approximately 30% of patients develop drug-resistant epilepsy, epileptogenic zones may remain occult on conventional magnetic resonance imaging, and surgical outcomes vary substantially. Multimodal artificial intelligence (AI) can integrate structural and functional MRI, positron emission tomography, diffusion imaging, electroencephalography, intracranial electrophysiology, and clinical information to support quantitative decision-making. This targeted narrative review synthesizes published evidence on three clinical tasks: prediction of antiseizure medication response, localization of epileptogenic zones, and prediction of surgical outcomes. We summarize machine-learning, deep-learning, and brain-network approaches, together with representative validation strategies and performance metrics. Current studies demonstrate promising task-specific performance, but their results should not be compared directly because cohorts, endpoints, modalities, and validation designs differ markedly. Major barriers include small single-center datasets, inconsistent acquisition and annotation, incomplete multimodal data, limited external validation, and insufficient interpretability. Future priorities include standardized multicenter datasets, prospective evaluation, privacy-preserving collaboration, interpretable fusion models, and clinically integrated human-AI workflows. At present, multimodal AI should be regarded as supervised decision support rather than a replacement for specialist interpretation.