The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis

Bach Xuan Tran, Roger S. McIntyre, Carl A. Latkin, Hai Thanh Phan, Giang Thu Vu, Huong Lan Thi Nguyen, Kenneth K. Gwee, Cyrus S. H. Ho and Roger C. M. Ho
Additional contact information
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
Carl A. Latkin: Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA
Hai Thanh Phan: Institute for Global Health Innovations, Duy Tan University, Da Nang 550000, Vietnam
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
Kenneth K. Gwee: 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
Roger C. M. Ho: Center of Excellence in Evidence-based Medicine, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam

IJERPH, 2019, vol. 16, issue 12, 1-16

Abstract: Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depression. We performed a bibliometric analysis of the current research landscape, which objectively evaluates the productivity of global researchers or institutions in this field, along with exploratory factor analysis (EFA) and latent dirichlet allocation (LDA). From 2010 onwards, the total number of papers and citations on using AI to manage depressive disorder have risen considerably. In terms of global AI research network, researchers from the United States were the major contributors to this field. Exploratory factor analysis showed that the most well-studied application of AI was the utilization of machine learning to identify clinical characteristics in depression, which accounted for more than 60% of all publications. Latent dirichlet allocation identified specific research themes, which include diagnosis accuracy, structural imaging techniques, gene testing, drug development, pattern recognition, and electroencephalography (EEG)-based diagnosis. Although the rapid development and widespread use of AI provide various benefits for both health providers and patients, interventions to enhance privacy and confidentiality issues are still limited and require further research.

Keywords: artificial intelligence; machine learning; depression; depressive disorders; bibliometric analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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