Fangyuan Xu ¹ Ziliang Dai ² Yu Ye ³ Peijia Hu ^4* Hongliang Cheng ^4,5*

• ¹ The First Clinical Medical School, Anhui University of Chinese Medicine, Hefei, Anhui Province, China
• ² Department of Rehabilitation Medicine, The Second Hospital of Wuhan Iron and Steel (Group) Corp., Wuhan, Hebei Province, China
• ³ The Second Clinical Medical School, Anhui University of Chinese Medicine, Hefei, Anhui Province, China
• ⁴ The Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
• ⁵ Anhui Province Key Laboratory of Meridian Viscera Correlationship, Hefei, China

Background: Stroke stands as a prominent cause of mortality and disability worldwide, posing a major public health concern. Recent years have witnessed rapid advancements in artificial intelligence (AI). Studies have explored the utilization of AI in imaging analysis, assistive rehabilitation, treatment, clinical decision-making, and outcome and risk prediction concerning stroke. However, there is still a lack of systematic bibliometric analysis to discern the current research status, hotspots, and possible future development trends of AI applications in stroke. Methods: The publications on the application of AI in stroke were retrieved from the Web of Science Core Collection, spanning 2004 to 2024. Only articles or reviews published in English were included in this study. Subsequently, a manual screening process was employed to eliminate literature not pertinent to the topic. Visualization diagrams for comprehensive and in-depth analysis of the included literature were generated using CiteSpace, VOSviewer, and Charticulator. Results: This bibliometric analysis included a total of 2447 papers, and the annual publication volume shows a notable upward trajectory. The most prolific authors, countries, and institutions are Dukelow, Sean P., China, and the University of Calgary, respectively, making significant contributions to the advancement of this field. Notably, stable collaborative networks among authors and institutions have formed. Through clustering and citation burst analysis of keywords and references, the current research hotspots have been identified, including machine learning, deep learning, and AI applications in stroke rehabilitation and imaging for early diagnosis. Moreover, emerging research trends focus on machine learning as well as stroke outcomes and risk prediction. Conclusion: This study provides a comprehensive and in-depth analysis of the literature regarding AI in stroke, facilitating a rapid comprehension of the development status, cooperative networks, and research priorities within the field. Furthermore, our analysis may provide a certain reference and guidance for future research endeavors.