From Ambient Assistance to Data-Driven Precision in AI-Enabled Alzheimer’s Care (2004–2025): A Bibliometric Perspective

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ID: 317294
2026
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Abstract
BACKGROUND AND OBJECTIVES: Population aging and the rising prevalence of Alzheimer's Disease and Related Dementias (AD/ADRD) have created an urgent need for innovative care solutions. Artificial Intelligence (AI) has emerged as a critical tool, yet a macro-level quantitative analysis of its evolutionary trajectory in geriatric care remains scarce. This study conducts a comprehensive bibliometric analysis to map the knowledge domain of AI-empowered geriatric care for AD/ADRD from 2004 to 2025, elucidating spatial distributions, evolutionary trajectories, and emerging research frontiers. RESEARCH DESIGN AND METHODS: A total of 1,197 eligible records were retrieved from the Web of Science Core Collection (WoSCC). Bibliometric analyses including co-occurrence networks, burst detection, and thematic evolution were conducted utilizing CiteSpace, VOSviewer, and the Bibliometrix R package. RESULTS: The 1,197 documents (402 sources; 7,268 authors) show an annual growth rate of 30.36%, peaking at 262 publications in 2025. The United States (242 articles; 6,640 citations) and China (213 articles; 3,649 citations) dominate global output, with Harvard University and the University of London as primary hubs, though the Global South remains underrepresented. Thematic evolution reveals three phases: early "smart homes" and "activity detection"; middle "home monitoring" and "social robots"; and a current frontier of "explainable AI" and biomarker-driven risk assessment. Keyword bursts confirm "tau" and "intervention" as leading hotspots to 2025. DISCUSSION AND IMPLICATIONS: AI in AD/ADRD care is transitioning from environmental assistance to neuro-precision diagnostics and therapeutics. Future research should integrate ethical, multimodal AI to bridge care gaps and facilitate high-quality aging-in-place.
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Authors Bingnan Du, Qi Li, Xueying Li, Xiaoxi Liu, Ke Rang Zhang, Yifan Gong, Jiangdian Song, Hui Wu
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Journal the gerontologist
Year 2026
DOI
10.1093/geront/gnag135
URL
https://doi.org/10.1093/geront/gnag135
Keywords Keywords not found

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