Bösel Julian, Mathur Rohan, Cheng Lin, Varelas Marianna S, Hobert Markus A, Suarez José I
Department of Neurology, University Hospital Heidelberg, Heidelberg, Germany.
Departments of Neurology and Neurocritical Care, Johns Hopkins University Hospital, Baltimore, MD, USA.
Neurol Res Pract. 2025 Feb 17;7(1):11. doi: 10.1186/s42466-025-00367-2.
Artificial Intelligence is influencing medicine on all levels. Neurology, one of the most complex and progressive medical disciplines, is no exception. No longer limited to neuroimaging, where data-driven approaches were initiated, machine and deep learning methodologies are taking neurologic diagnostics, prognostication, predictions, decision making and even therapy to very promising potentials.
In this review, the basic principles of different types of Artificial Intelligence and the options to apply them to neurology are summarized. Examples of noteworthy studies on such applications are presented from the fields of acute and intensive care neurology, stroke, epilepsy, and movement disorders. Finally, these potentials are matched with risks and challenges jeopardizing ethics, safety and equality, that need to be heeded by neurologists welcoming Artificial Intelligence to their field of expertise.
Artificial intelligence is and will be changing neurology. Studies need to be taken to the prospective level and algorithms undergo federated learning to reach generalizability. Neurologists need to master not only the benefits but also the risks in safety, ethics and equity of such data-driven form of medicine.
人工智能正在影响医学的各个层面。神经学作为最复杂且发展迅速的医学学科之一，也不例外。机器学习和深度学习方法不再局限于最初采用数据驱动方法的神经影像学领域，而是在神经诊断、预后评估、预测、决策甚至治疗方面展现出非常可观的潜力。
本综述总结了不同类型人工智能的基本原理以及将其应用于神经学的方法。介绍了急性和重症监护神经学、中风、癫痫和运动障碍领域中此类应用的一些值得关注的研究实例。最后，将这些潜力与危及伦理、安全和平等的风险与挑战进行了匹配，神经科医生在将人工智能引入其专业领域时需要注意这些问题。
人工智能正在并将继续改变神经学。研究需要达到前瞻性水平，算法需要进行联邦学习以实现通用性。神经科医生不仅要掌握这种数据驱动型医学在安全性、伦理和公平性方面的益处，还要了解其风险。
