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

Author

Listed:
  • 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 21218, 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)

  • 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
    Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
    Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore 119077, Singapore)

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.

Suggested Citation

  • 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, 2019. "The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis," IJERPH, MDPI, vol. 16(12), pages 1-16, June.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:12:p:2150-:d:240679
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    References listed on IDEAS

    as
    1. Roger C. M. Ho & Anna C. Chua & Bach X. Tran & Carol C. Choo & Syeda Fabeha Husain & Giang T. Vu & Roger S. McIntyre & Cyrus S. H. Ho, 2018. "Factors Associated with the Risk of Developing Coronary Artery Disease in Medicated Patients with Major Depressive Disorder," IJERPH, MDPI, vol. 15(10), pages 1-13, September.
    2. Thuc Thi Minh Vu & Thieu Van Le & Anh Kim Dang & Long Hoang Nguyen & Binh Cong Nguyen & Bach Xuan Tran & Carl A. Latkin & Cyrus S. H. Ho & Roger C. M. Ho, 2019. "Socioeconomic Vulnerability to Depressive Symptoms in Patients with Chronic Hepatitis B," IJERPH, MDPI, vol. 16(2), pages 1-10, January.
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    Cited by:

    1. Yiming Xiao & Han Wu & Guohua Wang & Hong Mei, 2021. "Mapping the Worldwide Trends on Energy Poverty Research: A Bibliometric Analysis (1999–2019)," IJERPH, MDPI, vol. 18(4), pages 1-22, February.
    2. Tamíris Pacheco da Costa & James Gillespie & Xavier Cama-Moncunill & Shane Ward & Joan Condell & Ramakrishnan Ramanathan & Fionnuala Murphy, 2022. "A Systematic Review of Real-Time Monitoring Technologies and Its Potential Application to Reduce Food Loss and Waste: Key Elements of Food Supply Chains and IoT Technologies," Sustainability, MDPI, vol. 15(1), pages 1-27, December.
    3. Ana Suárez & Alberto Adanero & Víctor Díaz-Flores García & Yolanda Freire & Juan Algar, 2022. "Using a Virtual Patient via an Artificial Intelligence Chatbot to Develop Dental Students’ Diagnostic Skills," IJERPH, MDPI, vol. 19(14), pages 1-14, July.
    4. Abderahman Rejeb & John G. Keogh & Suhaiza Zailani & Horst Treiblmaier & Karim Rejeb, 2020. "Blockchain Technology in the Food Industry: A Review of Potentials, Challenges and Future Research Directions," Logistics, MDPI, vol. 4(4), pages 1-26, October.
    5. P. V. Thayyib & Rajesh Mamilla & Mohsin Khan & Humaira Fatima & Mohd Asim & Imran Anwar & M. K. Shamsudheen & Mohd Asif Khan, 2023. "State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary," Sustainability, MDPI, vol. 15(5), pages 1-38, February.

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