Josephson Colin B, Aronica Eleonora, Beniczky Sandor, Boyce Danielle, Cavalleri Gianpiero, Denaxas Spiros, French Jacqueline, Jehi Lara, Koh Hyunyong, Kwan Patrick, McDonald Carrie, Mitchell James W, Rampp Stefan, Sadleir Lynette, Sisodiya Sanjay M, Wang Irene, Wiebe Samuel, Yasuda Clarissa, Youngerman Brett
Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.
Epileptic Disord. 2024 Dec;26(6):733-752. doi: 10.1002/epd2.20288. Epub 2024 Oct 24.
Epilepsy care generates multiple sources of high-dimensional data, including clinical, imaging, electroencephalographic, genomic, and neuropsychological information, that are collected routinely to establish the diagnosis and guide management. Thanks to high-performance computing, sophisticated graphics processing units, and advanced analytics, we are now on the cusp of being able to use these data to significantly improve individualized care for people with epilepsy. Despite this, many clinicians, health care providers, and people with epilepsy are apprehensive about implementing Big Data and accompanying technologies such as artificial intelligence (AI). Practical, ethical, privacy, and climate issues represent real and enduring concerns that have yet to be completely resolved. Similarly, Big Data and AI-related biases have the potential to exacerbate local and global disparities. These are highly germane concerns to the field of epilepsy, given its high burden in developing nations and areas of socioeconomic deprivation. This educational paper from the International League Against Epilepsy's (ILAE) Big Data Commission aims to help clinicians caring for people with epilepsy become familiar with how Big Data is collected and processed, how they are applied to studies using AI, and outline the immense potential positive impact Big Data can have on diagnosis and management.
癫痫护理会产生多个高维数据源，包括临床、影像、脑电图、基因组和神经心理学信息，这些数据是在日常诊断和管理中常规收集的。得益于高性能计算、先进的图形处理单元和高级分析技术，我们目前正处于能够利用这些数据显著改善癫痫患者个性化护理的关键时刻。尽管如此，许多临床医生、医疗服务提供者以及癫痫患者对实施大数据及人工智能（AI）等相关技术仍心存顾虑。实际操作、伦理、隐私和气候问题都是切实存在且长期未得到完全解决的问题。同样，大数据和人工智能相关的偏差有可能加剧地区和全球的不平等。鉴于癫痫在发展中国家以及社会经济贫困地区负担沉重，这些问题与癫痫领域高度相关。国际抗癫痫联盟（ILAE）大数据委员会撰写的这篇教育论文旨在帮助照顾癫痫患者的临床医生熟悉大数据的收集和处理方式、如何将其应用于人工智能研究，并概述大数据在诊断和管理方面可能产生的巨大积极影响。
