Yeo Melissa, Kok Hong Kuan, Kutaiba Numan, Maingard Julian, Thijs Vincent, Tahayori Bahman, Russell Jeremy, Jhamb Ashu, Chandra Ronil V, Brooks Mark, Barras Christen D, Asadi Hamed
School of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
Interventional Radiology Service, Department of Radiology, Northern Health, Melbourne, Victoria, Australia.
J Med Imaging Radiat Oncol. 2021 May 28. doi: 10.1111/1754-9485.13193.
Artificial intelligence (AI) is making a profound impact in healthcare, with the number of AI applications in medicine increasing substantially over the past five years. In acute stroke, it is playing an increasingly important role in clinical decision-making. Contemporary advances have increased the amount of information - both clinical and radiological - which clinicians must consider when managing patients. In the time-critical setting of acute stroke, AI offers the tools to rapidly evaluate and consolidate available information, extracting specific predictions from rich, noisy data. It has been applied to the automatic detection of stroke lesions on imaging and can guide treatment decisions through the prediction of tissue outcomes and long-term functional outcomes. This review examines the current state of AI applications in stroke, exploring their potential to reform stroke care through clinical decision support, as well as the challenges and limitations which must be addressed to facilitate their acceptance and adoption for clinical use.
人工智能（AI）正在对医疗保健产生深远影响，在过去五年中，医学领域的人工智能应用数量大幅增加。在急性卒中方面，它在临床决策中发挥着越来越重要的作用。当代的进展增加了临床医生在管理患者时必须考虑的信息量，包括临床信息和放射学信息。在急性卒中这种时间紧迫的情况下，人工智能提供了快速评估和整合可用信息的工具，能从丰富但嘈杂的数据中提取特定预测。它已被应用于成像上卒中病变的自动检测，并可通过预测组织结果和长期功能结果来指导治疗决策。本综述探讨了人工智能在卒中领域应用的现状，探讨其通过临床决策支持改革卒中护理的潜力，以及为促进其被接受和用于临床而必须解决的挑战和局限性。
