Zhang Yabin, Yu Lei, Lv Yuting, Yang Tiantian, Guo Qi
Department of Special Services, The 960th Hospital of the PLA Joint Logistics Support Force, Jinan, Shandong, China.
Campus Clinic, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Front Neurol. 2025 Jul 16;16:1607924. doi: 10.3389/fneur.2025.1607924. eCollection 2025.
This bibliometric review examines the evolving landscape of artificial intelligence (AI) in neurodegenerative diseases research from 2000 to March 16, 2025, utilizing data from 1,402 publications (1,159 articles, 243 reviews) indexed in the Web of Science Core Collection. Through advanced tools - VOSviewer, CiteSpace, and Bibliometrix R - the study maps collaboration networks, keyword trends, and knowledge trajectories. Results reveal exponential growth post-2017, driven by advancements in deep learning and multimodal data integration. The United States (25.96%) and China (24.11%) dominate publication volume, while the UK exhibits the highest collaboration centrality (0.24) and average citations per publication (31.68). Core journals like and published the most articles in this field. Highly cited publications and burst references highlight important milestones in the development history. High-frequency keywords include "alzheimer's disease," "parkinson's disease," "magnetic resonance imaging," "convolutional neural network," "biomarkers," "dementia," "classification," "mild cognitive impairment," "neuroimaging," and "feature extraction." Key hotspots include intelligent neuroimaging analysis, machine learning methodological iterations, molecular mechanisms and drug discovery, and clinical decision support systems for early diagnosis. Future priorities encompass advanced deep learning architectures, multi-omics integration, explainable AI systems, digital biomarker-based early detection, and transformative technologies including transformers and telemedicine. This analysis delineates AI's transformative role in optimizing diagnostics and accelerating therapeutic innovation, while advocating for enhanced interdisciplinary collaboration to bridge computational advances with clinical translation.
本文献计量学综述利用科学网核心合集中索引的1402篇出版物（1159篇文章、243篇综述）的数据，审视了2000年至2025年3月16日期间人工智能（AI）在神经退行性疾病研究领域不断演变的格局。通过先进工具——VOSviewer、CiteSpace和Bibliometrix R，该研究绘制了合作网络、关键词趋势和知识轨迹。结果显示，受深度学习和多模态数据整合进展的推动，2017年后呈现指数级增长。美国（25.96%）和中国（24.11%）在出版物数量上占主导地位，而英国的合作中心性最高（0.24），且每篇出版物的平均被引次数最多（31.68）。诸如《 》和《 》等核心期刊在该领域发表的文章最多。高被引出版物和突发参考文献突出了发展历史中的重要里程碑。高频关键词包括“阿尔茨海默病”“帕金森病”“磁共振成像”“卷积神经网络”“生物标志物”“痴呆”“分类”“轻度认知障碍”“神经成像”和“特征提取”。关键热点包括智能神经成像分析、机器学习方法的迭代、分子机制与药物发现以及早期诊断的临床决策支持系统。未来的重点包括先进的深度学习架构、多组学整合、可解释人工智能系统、基于数字生物标志物的早期检测以及包括Transformer和远程医疗在内的变革性技术。本分析描绘了人工智能在优化诊断和加速治疗创新方面的变革性作用，同时倡导加强跨学科合作，以弥合计算进展与临床转化之间的差距。
