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, Roger C.M. Ho

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number2150
JournalInternational journal of environmental research and public health
Volume16
Issue number12
DOIs
Publication statusPublished - Jun 2 2019

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Keywords

  • Artificial intelligence
  • Bibliometric analysis
  • Depression
  • Depressive disorders
  • Machine learning

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

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