Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000

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 - VO...

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Main Authors: Yabin Zhang, Lei Yu, Yuting Lv, Tiantian Yang, Qi Guo
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Neurology
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Online Access:https://www.frontiersin.org/articles/10.3389/fneur.2025.1607924/full
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author Yabin Zhang
Lei Yu
Yuting Lv
Tiantian Yang
Qi Guo
author_facet Yabin Zhang
Lei Yu
Yuting Lv
Tiantian Yang
Qi Guo
author_sort Yabin Zhang
collection DOAJ
description 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 Scientific Reports and Frontiers in Aging Neuroscience 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.
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spelling doaj-art-dbb7ad27b70c40eca67d291f21e5ce692025-07-23T15:46:26ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-07-011610.3389/fneur.2025.16079241607924Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000Yabin Zhang0Lei Yu1Yuting Lv2Tiantian Yang3Qi Guo4Department of Special Services, The 960th Hospital of the PLA Joint Logistics Support Force, Jinan, Shandong, ChinaDepartment of Special Services, The 960th Hospital of the PLA Joint Logistics Support Force, Jinan, Shandong, ChinaCampus Clinic, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, ChinaDepartment of Traditional Chinese Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaDepartment of Special Services, The 960th Hospital of the PLA Joint Logistics Support Force, Jinan, Shandong, ChinaThis 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 Scientific Reports and Frontiers in Aging Neuroscience 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.https://www.frontiersin.org/articles/10.3389/fneur.2025.1607924/fullartificial intelligenceneurodegenerative diseasesbibliometricVOSviewerCiteSpacebibliometrix R
spellingShingle Yabin Zhang
Lei Yu
Yuting Lv
Tiantian Yang
Qi Guo
Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000
Frontiers in Neurology
artificial intelligence
neurodegenerative diseases
bibliometric
VOSviewer
CiteSpace
bibliometrix R
title Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000
title_full Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000
title_fullStr Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000
title_full_unstemmed Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000
title_short Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000
title_sort artificial intelligence in neurodegenerative diseases research a bibliometric analysis since 2000
topic artificial intelligence
neurodegenerative diseases
bibliometric
VOSviewer
CiteSpace
bibliometrix R
url https://www.frontiersin.org/articles/10.3389/fneur.2025.1607924/full
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AT tiantianyang artificialintelligenceinneurodegenerativediseasesresearchabibliometricanalysissince2000
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