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The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis

Author

Listed:
  • Bach Xuan Tran

    (Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi 100000, Vietnam
    Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA)

  • Carl A. Latkin

    (Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA)

  • Giang Thu Vu

    (Center of Excellence in Evidence-Based Medicine, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam)

  • Huong Lan Thi Nguyen

    (Institute for Global Health Innovations, Duy Tan University, Da Nang 550000, Vietnam)

  • Son Nghiem

    (Centre for Applied Health Economics, Griffith University, Queensland 4111, Australia)

  • Ming-Xuan Tan

    (Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119074, Singapore)

  • Zhi-Kai Lim

    (Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119074, Singapore)

  • Cyrus S.H. Ho

    (Department of Psychological Medicine, National University Hospital, Singapore 119074, Singapore)

  • Roger C.M. Ho

    (Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119074, Singapore
    Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam
    Institute for Health Innovation and Technology (iHealthtech), Singapore 119074, Singapore)

Abstract

The applications of artificial intelligence (AI) in aiding clinical decision-making and management of stroke and heart diseases have become increasingly common in recent years, thanks in part to technological advancements and the heightened interest of the research and medical community. This study aims to provide a comprehensive picture of global trends and developments of AI applications relating to stroke and heart diseases, identifying research gaps and suggesting future directions for research and policy-making. A novel analysis approach that combined bibliometrics analysis with a more complex analysis of abstract content using exploratory factor analysis and Latent Dirichlet allocation, which uncovered emerging research domains and topics, was adopted. Data were extracted from the Web of Science database. Results showed topics with the most compelling growth to be AI for big data analysis, robotic prosthesis, robotics-assisted stroke rehabilitation, and minimally invasive surgery. The study also found an emerging landscape of research that was centered on population-specific and early detection of stroke and heart disease. Application of AI in health behavior tracking and improvement as well as the use of robotics in medical diagnostics and prognostication have also been found to attract significant research attention. In light of these findings, it is suggested that the currently under-researched issues of data management, AI model reliability, as well as validation of its clinical utility, need to be further explored in future research and policy decisions to maximize the benefits of AI applications in stroke and heart diseases.

Suggested Citation

  • Bach Xuan Tran & Carl A. Latkin & Giang Thu Vu & Huong Lan Thi Nguyen & Son Nghiem & Ming-Xuan Tan & Zhi-Kai Lim & Cyrus S.H. Ho & Roger C.M. Ho, 2019. "The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis," IJERPH, MDPI, vol. 16(15), pages 1-14, July.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:15:p:2699-:d:252563
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    References listed on IDEAS

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    1. Hyeonyong Hae & Soo-Jin Kang & Won-Jang Kim & So-Yeon Choi & June-Goo Lee & Youngoh Bae & Hyungjoo Cho & Dong Hyun Yang & Joon-Won Kang & Tae-Hwan Lim & Cheol Hyun Lee & Do-Yoon Kang & Pil Hyung Lee &, 2018. "Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-19, November.
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    Cited by:

    1. Elena García-García & Gracia María González-Romero & Encarna M. Martín-Pérez & Enrique de Dios Zapata Cornejo & Gema Escobar-Aguilar & Marlon Félix Cárdenas Bonnet, 2021. "Real-World Data and Machine Learning to Predict Cardiac Amyloidosis," IJERPH, MDPI, vol. 18(3), pages 1-15, January.
    2. Minxi Wang & Ping Liu & Rui Zhang & Zhi Li & Xin Li, 2020. "A Scientometric Analysis of Global Health Research," IJERPH, MDPI, vol. 17(8), pages 1-19, April.
    3. Fuentealba, Diego & Flores-Fernández, Cherie & Troncoso, Elizabeth & Estay, Humberto, 2023. "Technological tendencies for lithium production from salt lake brines: Progress and research gaps to move towards more sustainable processes," Resources Policy, Elsevier, vol. 83(C).

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