Evidence map›Paper›PMID 32723713›Full record

ArticleJournal of medical Internet research2020

Artificial Intelligence in Health Care: Bibliometric Analysis.

Yuqi Guo, Zhichao Hao, Shichong Zhao, Jiaqi Gong, Fan Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 155 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
155citing papers in PubMed, 7 pooled it
–field-weighted citation impact
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

155 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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  11. A conceptual framework for measuring AI health equity.International journal for equity in health · 2026
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95 more citing papers are in PubMed but not listed here.

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.

Yuqi GuoSchool of Social Work, University of North Carolina at Charlotte, Charlotte, NC, United States.ORCID 0000-0002-7060-8404
Zhichao HaoSchool of Social Work, The University of Alabama, Tuscaloosa, AL, United States.ORCID 0000-0002-1636-5245
Shichong ZhaoSocial Welfare Program, School of Public Administration, Dongbei University of Finance and Economics, Dalian, China.ORCID 0000-0001-5964-4853
Jiaqi GongDepartment of Information Systems, University of Maryland, Baltimore, MD, United States.ORCID 0000-0001-9694-2518
Fan YangSocial Welfare Program, School of Public Administration, Dongbei University of Finance and Economics, Dalian, China.ORCID 0000-0003-4250-8446

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs a critical driving power to promote health care, the health care-related artificial intelligence (AI) literature is growing rapidly.

objectiveThe purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care-related AI publications.

methodsThe Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software.

resultsThe search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019.

conclusionsThis analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care-related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

Indexed as

BibliometricsArtificial IntelligenceDelivery of Health CareHumansartificial intelligencebibliometric analysishealth caremachine learningneural networkstelehealth

Identifiers

PMID32723713
PMCPMC7424481

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.