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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Frontiers Media S.A.
2025-07-01
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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. |
format | Article |
id | doaj-art-dbb7ad27b70c40eca67d291f21e5ce69 |
institution | Matheson Library |
issn | 1664-2295 |
language | English |
publishDate | 2025-07-01 |
publisher | Frontiers Media S.A. |
record_format | Article |
series | Frontiers in Neurology |
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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