Song Sijia, Li Tong, Lin Wei, Liu Ran, Zhang Yujie
School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
School of Biomedical Engineering, Tsinghua University, Beijing, China.
Front Neurosci. 2025 Feb 14;19:1511350. doi: 10.3389/fnins.2025.1511350. eCollection 2025.
Understanding how artificial intelligence (AI) is employed to predict, diagnose, and perform relevant analyses in Alzheimer's disease research is a rapidly evolving field. This study integrated and analyzed the relevant literature from the Science Citation Index (SCI) and Social Science Citation Index (SSCI) on the application of AI in Alzheimer's disease (AD), covering publications from 2004 to 2023.
This study aims to identify the key research hotspots and trends of the application of AI in AD over the past 20 years through a bibliometric analysis.
Using the Web of Science Core Collection database, we conducted a comprehensive visual analysis of literature on AI and AD published between January 1, 2004, and December 31, 2023. The study utilized Excel, Scimago Graphica, VOSviewer, and CiteSpace software to visualize trends in annual publications and the distribution of research by countries, institutions, journals, references, authors, and keywords related to this topic.
A total of 2,316 papers were obtained through the research process, with a significant increase in publications observed since 2018, signaling notable growth in this field. The United States, China, and the United Kingdom made notable contributions to this research area. The University of London led in institutional productivity with 80 publications, followed by the University of California System with 74 publications. Regarding total publications, the was the most prolific while ranked as the most cited journal. Shen Dinggang was the top author in both total publications and average citations. Analysis of reference and keyword highlighted research hotspots, including the identification of various stages of AD, early diagnostic screening, risk prediction, and prediction of disease progression. The "task analysis" keyword emerged as a research frontier from 2021 to 2023.
Research on AI applications in AD holds significant potential for practical advancements, attracting increasing attention from scholars. Deep learning (DL) techniques have emerged as a key research focus for AD diagnosis. Future research will explore AI methods, particularly task analysis, emphasizing integrating multimodal data and utilizing deep neural networks. These approaches aim to identify emerging risk factors, such as environmental influences on AD onset, predict disease progression with high accuracy, and support the development of prevention strategies. Ultimately, AI-driven innovations will transform AD management from a progressive, incurable state to a more manageable and potentially reversible condition, thereby improving healthcare, rehabilitation, and long-term care solutions.
了解人工智能（AI）如何用于阿尔茨海默病研究中的预测、诊断及进行相关分析是一个快速发展的领域。本研究整合并分析了科学引文索引（SCI）和社会科学引文索引（SSCI）中关于AI在阿尔茨海默病（AD）应用的相关文献，涵盖2004年至2023年的出版物。
本研究旨在通过文献计量分析确定过去20年AI在AD应用中的关键研究热点和趋势。
利用科学网核心合集数据库，对2004年1月1日至2023年12月31日期间发表的关于AI与AD的文献进行全面可视化分析。该研究使用Excel、Scimago Graphica、VOSviewer和CiteSpace软件来可视化年度出版物趋势以及按国家、机构、期刊、参考文献、作者和与该主题相关的关键词划分的研究分布情况。
通过研究过程共获得2316篇论文，自2018年以来观察到出版物数量显著增加，表明该领域有显著增长。美国、中国和英国对该研究领域做出了显著贡献。伦敦大学在机构产出方面领先，有80篇出版物，其次是加利福尼亚大学系统，有74篇出版物。就总出版物数量而言，[此处原文缺失具体期刊名称]是最多产的，而[此处原文缺失具体期刊名称]是被引用最多的期刊。沈定刚在总出版物数量和平均被引次数方面均位居榜首。对参考文献和关键词的分析突出了研究热点，包括AD各个阶段的识别、早期诊断筛查、风险预测以及疾病进展预测。“任务分析”关键词在2021年至2023年期间成为研究前沿。
AI在AD中的应用研究具有显著的实际进展潜力，吸引了学者们越来越多的关注。深度学习（DL）技术已成为AD诊断的关键研究重点。未来研究将探索AI方法，特别是任务分析，强调整合多模态数据并利用深度神经网络。这些方法旨在识别新出现的风险因素，如环境对AD发病的影响，高精度预测疾病进展，并支持预防策略的制定。最终，AI驱动的创新将把AD管理从一种进行性、不可治愈的状态转变为一种更易于管理且可能可逆的状态，从而改善医疗保健、康复和长期护理解决方案。
