Peng Yun, Zhao Zhen, Rao Yutong, Sun Ke, Zou Jiayi, Liu Guanqing
Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Jiangxi Provincial Key Laboratory of Intelligent Medical Imaging, Nanchang, China.
Digit Health. 2025 Feb 28;11:20552076251323833. doi: 10.1177/20552076251323833. eCollection 2025 Jan-Dec.
Currently, artificial intelligence (AI) has been widely used for the prediction, diagnosis, evaluation and rehabilitation of stroke. However, the quantitative and qualitative description of this field is still lacking.
This study aimed to summarize and elucidate the research status and changes in hotspots on the application of AI in stroke over the past 20 years through bibliometric analysis.
Publications on the application of AI in stroke in the past two decades were retrieved from the Web of Science Core Collection. Microsoft Excel was used to analyze the annual publication volume. The cooperation network map among countries/regions was generated on an online platform (https://bibliometric.com/). CiteSpace was used to visualize the co-occurrence of institutions and analyze the timeline view of references and burst keywords. The network visualization map of keywords co-occurrence was generated by VOSviewer.
A total of 4437 publications were included. The annual number of published documents shows an upwards trend. The USA published the most documents and has the top 3 most productive institutions. Journal of Neuroengineering and Rehabilitation and Stroke are the journals with the most publications and citations, respectively. The keywords co-occurrence network classified the keywords into four themes, that is "rehabilitation," "machine learning," "recovery" and "upper limb function." The top 3 keywords with the strongest burst strength were "arm," "upper limb" and "therapy." The most recent keywords that burst after 2020 and last until 2023 included "scores," "machine learning," "natural language processing" and "atrial fibrillation."
The USA shows a leading position in this field. At present and in the next few years, research in this field may focus on the prediction/rapid diagnosis of potential stroke patients by using machine learning, deep learning and natural language processing.
目前，人工智能（AI）已广泛应用于中风的预测、诊断、评估及康复。然而，该领域的定量和定性描述仍较为缺乏。
本研究旨在通过文献计量分析总结并阐明过去20年AI在中风应用方面的研究现状及热点变化。
从科学引文索引核心合集检索过去二十年中AI在中风应用方面的出版物。使用微软Excel分析年度出版物数量。在在线平台（https://bibliometric.com/）上生成国家/地区间的合作网络图。使用CiteSpace可视化机构共现情况，并分析参考文献的时间线视图和突现关键词。通过VOSviewer生成关键词共现的网络可视化图。
共纳入4437篇出版物。年度发文数量呈上升趋势。美国发表的文献最多，且拥有排名前3的高产机构。《神经工程与康复杂志》和《中风》分别是发表文章和被引次数最多的期刊。关键词共现网络将关键词分为四个主题，即“康复”“机器学习”“恢复”和“上肢功能”。突现强度最强的前3个关键词是“手臂”“上肢”和“治疗”。2020年后出现且持续到2023年的最新突现关键词包括“评分”“机器学习”“自然语言处理”和“心房颤动”。
美国在该领域处于领先地位。目前及未来几年，该领域的研究可能集中于利用机器学习、深度学习和自然语言处理对潜在中风患者进行预测/快速诊断。
