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

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Center of Excellence in Evidence-based Medicine, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam
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Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi 100000, Vietnam
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Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
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Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
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Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON M5G 2C4, Canada
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Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
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Department of Toxicology and Pharmacology, University of Toronto, Toronto, ON M5S 1A8, Canada
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Institute for Global Health Innovations, Duy Tan University, Da Nang 550000, Vietnam
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Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
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Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam
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Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore 117599, Singapore
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Department of Psychological Medicine, National University Hospital, Singapore 119074, Singapore
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Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(6), 1982; https://doi.org/10.3390/ijerph17061982
Received: 18 December 2019 / Revised: 28 February 2020 / Accepted: 9 March 2020 / Published: 17 March 2020
(This article belongs to the Special Issue Bibliometric Studies and Worldwide Research Trends on Global Health)
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. View Full-Text
Keywords: artificial intelligence; machine learning; diabetes; bibliometric; LDA artificial intelligence; machine learning; diabetes; bibliometric; LDA
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Vu, G.T.; Tran, B.X.; McIntyre, R.S.; Pham, H.Q.; Phan, H.T.; Ha, G.H.; Gwee, K.K.; Latkin, C.A.; Ho, R.C.; Ho, C.S. Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAPRESEARCH). Int. J. Environ. Res. Public Health 2020, 17, 1982.

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