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

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 and Cyrus S.H. Ho
Additional contact information
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
Roger S. McIntyre: Institute of Medical Science, 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
Cyrus S.H. Ho: Department of Psychological Medicine, National University Hospital, Singapore 119074, Singapore

IJERPH, 2020, vol. 17, issue 6, 1-14

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.

Keywords: artificial intelligence; machine learning; diabetes; bibliometric; LDA (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/6/1982/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/6/1982/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:6:p:1982-:d:333628

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().