Advances in multimodal artificial intelligence models for epilepsy research

Abstract

Epilepsy, a prevalent and complex neurological disorder,poses significant challenges in clinical diagnosis and treatment due to its heterogeneous classifications,diverse etiologies,frequent lack of physician-witnessed episodes, and varied therapeutic approaches. The deep integration of Artificial Intelligence (AI) into healthcare has emerged as a promising solution. However, most existing epilepsy-related AI models primarily rely on single-data inputs, which struggle to address the complexity of epilepsy. Consequently, in recent years, the research and development of multimodal AI models-leveraging diverse and complex datasets, have gained momentum and demonstrated substantial advantages. This article reviews the current applications of multimodal AI models in epilepsy detection devices, diagnosis, treatment, and outcome prognosis, summarizing research hotspots and future trends. It aims to provide valuable insights for building more precise and comprehensive AI-driven epilepsy management systems.

Keywords: Artificial Intelligence; Epilepsy; Multimodal.