Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.

人工智能 (AI) 正在迅速改变医疗保健领域，其在癫痫领域的应用在过去十年中呈指数级增长。将人工智能融入癫痫管理有望彻底改变这种复杂疾病的诊断和治疗。然而，人工智能向神经病学临床实践的转化尚未成功，强调需要考虑迄今为止的进展并评估人工智能的挑战和局限性。在这篇综述中，我们概述了使用各种数据模式在癫痫领域开发的人工智能应用：神经影像、脑电图、电子健康记录、医疗设备和多模式数据集成。对于每一个，我们都考虑了潜在的应用，包括癫痫检测和预测、癫痫偏侧化、癫痫发作区域的定位以及手术或神经刺激干预的评估，并回顾了迄今为止开发的人工智能工具的性能。我们还讨论了将人工智能成功融入临床实践所必须解决的方法学考虑因素和挑战。我们的目标是概述该领域的现状，并为未来利用人工智能改善癫痫管理提供指导。