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Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAP RESEARCH )

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Listed:
  • Giang Thu Vu

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

  • 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)

  • Roger S. McIntyre

    (Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
    Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON M5G 2C4, Canada
    Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
    Department of Toxicology and Pharmacology, University of Toronto, Toronto, ON M5S 1A8, Canada)

  • Hai Quang Pham

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

  • Hai Thanh Phan

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

  • Giang Hai Ha

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

  • Kenneth K. Gwee

    (Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore)

  • Carl A. Latkin

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

  • Roger C.M. Ho

    (Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
    Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam
    Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore 117599, Singapore)

  • Cyrus S.H. Ho

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

Abstract

The rising prevalence and global burden of diabetes fortify the need for more comprehensive and effective management to prevent, monitor, and treat diabetes and its complications. Applying artificial intelligence in complimenting the diagnosis, management, and prediction of the diabetes trajectory has been increasingly common over the years. This study aims to illustrate an inclusive landscape of application of artificial intelligence in diabetes through a bibliographic analysis and offers future direction for research. Bibliometrics analysis was combined with exploratory factor analysis and latent Dirichlet allocation to uncover emergent research domains and topics related to artificial intelligence and diabetes. Data were extracted from the Web of Science Core Collection database. The results showed a rising trend in the number of papers and citations concerning AI applications in diabetes, especially since 2010. The nucleus driving the research and development of AI in diabetes is centered around developed countries, mainly consisting of the United States, which contributed 44.1% of the publications. Our analyses uncovered the top five emerging research domains to be: (i) use of artificial intelligence in diagnosis of diabetes, (ii) risk assessment of diabetes and its complications, (iii) role of artificial intelligence in novel treatments and monitoring in diabetes, (iv) application of telehealth and wearable technology in the daily management of diabetes, and (v) robotic surgical outcomes with diabetes as a comorbid. Despite the benefits of artificial intelligence, challenges with system accuracy, validity, and confidentiality breach will need to be tackled before being widely applied for patients’ benefits.

Suggested Citation

  • Giang Thu Vu & Bach Xuan Tran & Roger S. McIntyre & Hai Quang Pham & Hai Thanh Phan & Giang Hai Ha & Kenneth K. Gwee & Carl A. Latkin & Roger C.M. Ho & Cyrus S.H. Ho, 2020. "Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAP RESEARCH )," IJERPH, MDPI, vol. 17(6), pages 1-14, March.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:6:p:1982-:d:333628
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    References listed on IDEAS

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    1. M.J. Cobo & A.G. López-Herrera & E. Herrera-Viedma & F. Herrera, 2011. "Science mapping software tools: Review, analysis, and cooperative study among tools," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(7), pages 1382-1402, July.
    2. M.J. Cobo & A.G. López‐Herrera & E. Herrera‐Viedma & F. Herrera, 2011. "Science mapping software tools: Review, analysis, and cooperative study among tools," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(7), pages 1382-1402, July.
    3. Kerry A McBrien & Christopher Naugler & Noah Ivers & Robert G Weaver & David Campbell & Laura Desveaux & Brenda R Hemmelgarn & Alun L Edwards & Nathalie Saad & David Nicholas & Braden J Manns, 2017. "Barriers to care in patients with diabetes and poor glycemic control—A cross-sectional survey," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    4. Aghaei Chadegani, Arezoo & Salehi, Hadi & Md Yunus, Melor & Farhadi, Hadi & Fooladi, Masood & Farhadi, Maryam & Ale Ebrahim, Nader, 2013. "A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases," MPRA Paper 46898, University Library of Munich, Germany, revised 18 Mar 2013.
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