Depression Diagnosis with Artificial Intelligence: A Bibliometric Analysis
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Abstract
Background: Depression, a severe psychological disorder, exerts profound effects on an individual’s life. It saps energy, pleasures, and motivation leading individuals to helplessness and hopelessness, strained relationships, and increased thoughts of suicide ideation. Artificial intelligence has emerged as a potential tool in predictive analysis. With the analysis of big data, AI can detect patterns and indicators that aid in identifying depression with improved accuracy and efficacy. Since 2012, diagnosing depression with AI tools has become a hot topic of research. This study is a bibliometric analysis of the literature on diagnosing depression with artificial intelligence (AI) tools.
Methods: The authors geared up the four digital libraries: Pub Med, Web of Science, Google Scholar and Science Direct databases and applied filters to select relevant publications from 2014 to 2022. They used Microsoft Excel to analyze the data on publication growth, top contributors, keywords, and citations. VOSviewer was used to visualize collaborative maps between authors, institutions, affiliations, and hot topics related to the field.
Results: A total of 476 publications were used for this Bibliometric analysis. From 2019 to 2022, the growth of publications trend has seen a steady increase. The USA is the leading country in terms of publication count. The most cited author was Perlis, who published seven papers on depression and AI. The most affiliated institution was Harvard Medical School, which produced 28 publications. The most common keyword was “depression”, which appeared in 260 publications. The highly contributed journal was “Frontiers in Psychology” and the most cited paper was by Kessler et al., (2015).
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References
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