Evidence map›Paper›PMID 37351923›Full record

ArticleJournal of medical Internet research2023

Application of Artificial Intelligence in Geriatric Care: Bibliometric Analysis.

Jingjing Wang, Yiqing Liang, Songmei Cao, Peixuan Cai, Yimeng Fan

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 6 pooled it
2.3field-weighted citation impact, top 9% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 6 syntheses or guidelines pooled it, 59 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Jingjing WangDepartment of Nursing, The Affiliated Hospital of Jiangsu University, Zhenjiang, China.ORCID 0000-0001-8710-0298
Yiqing LiangDepartment of Nursing, The Affiliated Hospital of Jiangsu University, Zhenjiang, China.ORCID 0009-0005-1481-2194
Songmei CaoDepartment of Nursing, The Affiliated Hospital of Jiangsu University, Zhenjiang, China.ORCID 0000-0003-3178-4162
Peixuan CaiMedical College, Jiangsu University, Zhenjiang, China.ORCID 0009-0006-4025-5261
Yimeng FanDepartment of Nursing, The Affiliated Hospital of Jiangsu University, Zhenjiang, China.ORCID 0009-0003-9787-8177
Jiangsu University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) can improve the health and well-being of older adults and has the potential to assist and improve nursing care. In recent years, research in this area has been increasing. Therefore, it is necessary to understand the status of development and main research hotspots and identify the main contributors and their relationships in the application of AI in geriatric care via bibliometric analysis.

objectiveUsing bibliometric analysis, this study aims to examine the current research hotspots and collaborative networks in the application of AI in geriatric care over the past 23 years.

methodsThe Web of Science Core Collection database was used as a source. All publications from inception to August 2022 were downloaded. The external characteristics of the publications were summarized through HistCite and the Web of Science. Keywords and collaborative networks were analyzed using VOSviewers and Citespace.

resultsWe obtained a total of 230 publications. The works originated in 499 institutions in 39 countries, were published in 124 journals, and were written by 1216 authors. Publications increased sharply from 2014 to 2022, accounting for 90.87% (209/230) of all publications. The United States and the International Journal of Social Robotics had the highest number of publications on this topic. The 1216 authors were divided into 5 main clusters. Among the 230 publications, 4 clusters were modeled, including Alzheimer disease, aged care, acceptance, and the surveillance and treatment of diseases. Machine learning, deep learning, and rehabilitation had also become recent research hotspots.

conclusionsResearch on the application of AI in geriatric care has developed rapidly. The development of research and cooperation among countries/regions and institutions are limited. In the future, strengthening the cooperation and communication between different countries/regions and institutions may further drive this field's development. This study provides researchers with the information necessary to understand the current state, collaborative networks, and main research hotspots of the field. In addition, our results suggest a series of recommendations for future research.

Indexed as

Alzheimer DiseaseArtificial IntelligenceAgedBibliometricsCommunicationHumansMachine Learningartificial intelligencebibliometric analysisgeriatric careolder adults

Identifiers

PMID37351923
PMCPMC10337465
OpenAlexW4378567059

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.