Dataset Information

Artificial Intelligence in Health Care: Bibliometric Analysis.

ABSTRACT: BACKGROUND:As a critical driving power to promote health care, the health care-related artificial intelligence (AI) literature is growing rapidly. OBJECTIVE:The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care-related AI publications. METHODS:The 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. RESULTS:The 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. CONCLUSIONS:This 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.

SUBMITTER: Guo Y

PROVIDER: S-EPMC7424481 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Artificial Intelligence in Health Care: Bibliometric Analysis.

Guo Yuqi Y Hao Zhichao Z Zhao Shichong S Gong Jiaqi J Yang Fan F

Journal of medical Internet research 20200729 7

<h4>Background</h4>As a critical driving power to promote health care, the health care-related artificial intelligence (AI) literature is growing rapidly.<h4>Objective</h4>The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care-related AI publications.<h4>Methods</h4>The 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 b ...[more]

PMID: 32723713

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Project description:BackgroundThe field of critical care-related artificial intelligence (AI) research is rapidly gaining interest. However, there is still a lack of comprehensive bibliometric studies that measure and analyze scientific publications on a global scale. Network charts have traditionally been used to highlight author collaborations and coword phenomena (ACCP). It is necessary to determine whether chord network charts (CNCs) can provide a better understanding of ACCP, thus requiring clarification. This study aimed to achieve 2 objectives: evaluate global research trends in AI in intensive care medicine on publication outputs, coauthorships between nations, citations, and co-occurrences of keywords; and demonstrate the use of CNCs for ACCP in bibliometric analysis.MethodsThe web of science database was searched for a total of 1992 documents published between 2013 and 2022. The document type was limited to articles and article reviews, and titles and abstracts were screened for eligibility. The characteristics of the publications, including preferred journals, leading research countries, international collaborations, top institutions, and major keywords, were analyzed using the category-journal rank-authorship-L-index score and trend analysis. The 100 most highly cited articles are also listed in detail.ResultsBetween 2018 and 2022, there was a sharp increase in publications, which accounted for 92.8% (1849/1992) of all papers included in the study. The United States and China were responsible for nearly 50% (936/1992) of the total publications. The leading countries, institutes, departments, authors, and journals in terms of publications were the US, Massachusetts Gen Hosp (US), Medical School, Zhongheng Zhang (China), and Science Reports. The top 3 primary keywords denoting research hotspots for AI in critically ill patients were mortality, model, and intensive care unit, with mortality having the highest burst strength (4.49). The keywords risk and system showed the highest growth trend (0.98) in counts over the past 4 years.ConclusionsThis study provides valuable insights into the potential for ACCP and future research opportunities. For AI-based clinical research to become widely accepted in critical care practice, collaborative research efforts are necessary to strengthen the maturity and robustness of AI-driven models using CNCs for display.

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Dataset Information

Artificial Intelligence in Health Care: Bibliometric Analysis.

Publications

Artificial Intelligence in Health Care: Bibliometric Analysis.

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