Article Details

Title · Authors · Abstract · Keywords · DOI · References

Visualization Analysis of the Application of Artificial Intelligence in Alzheimer's Disease Research Based on CiteSpace

LAI Shengsheng1, ZHANG Gangping1, LIU Wenping1, CHEN Canyang1,2

(1.School of Medical Devices, Guangdong Food and Drug Vocational College, Guangzhou 510520, China; 2.School of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China)

Section：Information Application · Vol. 2025, Issue 9 · Pages：136-145,150 · 2096-4706（2025）09-0136-11 · CLC number：TP391.4 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2025.09.026

Abstract

This study explores the development trend and application hotspots of Artificial Intelligence in Alzheimer's disease research through bibliometric analysis. Based on the core database of Web of Science, 3 680 related literatures from 2013 to 2023 are retrieved. CiteSpace software is used for co-occurrence analysis and keyword clustering, and the publication trend, collaborations between countries and institutions, core authors and co-cited literatures are analyzed. The research results show that the application of Artificial Intelligence in the field of Alzheimer's disease mainly focuses on three directions of image data analysis and early diagnosis, multi-modal data fusion and brain network functional connection. At the same time, task analysis and transfer learning, as emerging hotspots, show the potential of Artificial Intelligence in individualized diagnosis and long-term disease management. From the analysis of the results, it can be seen that the application of Artificial Intelligence in the diagnosis and treatment of Alzheimer's disease is in a stage of rapid development. Future research will focus on the improvement of generalization ability and multi-modal data processing ability of the algorithm to provide more accurate diagnosis and individualized treatment.

Keywords

Alzheimer's disease; visual analysis; multi-modal data fusion; Neural Network; Machine Learning

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Funding

2024 Guangdong Medical Research Fund Project (A2024637); 2023 Guangdong Province Higher Vocational Education Teaching Quality and Teaching Reform Project (2023JG167); Guangdong Provincial Platform Research Project for Universities (2023KTSCX279)

About the authors

Lai Shengsheng (1982-), male, Han ethnicity, from Xinyi, Guangdong, lecturer, master's degree, research direction: medical image processing and artificial intelligence; Corresponding author: Chen Canyang (2004-), male, Han ethnicity, from Shenzhen, Guangdong Province, undergraduate student, research direction: medical image processing and artificial intelligence.