Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 24, 2024
Open Peer Review Period: Jan 24, 2024 - Mar 20, 2024
Date Accepted: Jan 20, 2025
(closed for review but you can still tweet)
Use of Artificial Intelligence, Internet of Things, and Edge Intelligence in Long-term Care for Older People: A Comprehensive Analysis through Bibliometric, Google Trends, and Content Analysis
ABSTRACT
Background:
The aging population poses significant challenges to the long-term care (LTC) sector, necessitating innovative solutions. Integrating Artificial Intelligence (AI) and Internet of Things (IoT) technologies in LTC offers promising avenues for enhancing care quality and efficiency.
Objective:
This study explores trends, patterns, and perceptions, both in academia and the public, regarding AI and IoT use in LTC over the past decade.
Methods:
We searched three databases, Web of Science, PubMed, and Scopus, to obtain a bibliometric analysis reflecting academic interests. Meanwhile, we used the top 12 most frequently used keywords in Google Trends searches to explore public interests.
Results:
Bibliometric analysis results showed that the USA is a leading contributor. Additionally, there are extensive international collaborations and thematic overlaps in gerontology. Furthermore, High correlations between academic interests and public interests were found among the keywords such as Long-term care (τ = 0.93, P<.001), Machine learning (τ = 0.80, P<.01), and Caregiver (τ = 0.76, P<.01).
Conclusions:
The study underscores the growing importance of AI and IoT in LTC, reflecting a convergence of academic research and public interest. This trend signals the vital role of these technologies in shaping future LTC practices.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.