ArticleDigital health
Mapping artificial intelligence in older adult care: A bibliometric analysis.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to map the intellectual structure and conceptual development of artificial intelligence (AI) research in older adult care by identifying current research fronts and emerging thematic priorities. Methods: A bibliometric analysis was conducted using 5,214 English-language journal articles indexed in the Web of Science Core Collection between 2004 and 2025. Bibliographic records were analysed using VOSviewer. Bibliographic coupling was employed to identify contemporary research fronts based on shared reference patterns, while co-word analysis examined conceptual structures and emerging research themes through keyword co-occurrence. Results: The field demonstrated substantial scholarly growth and influence, accumulating 79,858 citations, 74,375 non-self-citations, and an h-index of 102. Bibliographic coupling analysis identified five major research fronts: predictive health intelligence and assistive support; assistive robotics, cognitive support, and ageing-in-place technologies; fall detection, cognitive ageing, and assistive technologies for functional independence; service robots, smart environments, and human-AI acceptance; and rehabilitation, fall prevention, and age-friendly mobility environments. Co-word analysis revealed four dominant conceptual themes: health risk, frailty, and population-level ageing outcomes; AI-enabled care technologies and human-robot interaction; mobility, physical activity, and functional ageing; and machine learning, dementia, and cognitive impairment prediction. Conclusions: AI research in older adult care has evolved from isolated technological applications toward integrated socio-technical care ecosystems that support prevention, monitoring, diagnosis, rehabilitation, mobility, and quality-of-life enhancement. Future advances will depend on the development of human-centred, clinically meaningful, ethically governed, and socially sustainable AI-enabled care systems that address the multidimensional needs of ageing populations.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.