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.
Highlights
Depression is among the most common psychiatric conditions with a lifetime prevalence of10.8% [1]
The very first paper on the use of Artificial intelligence (AI) in depression was published in 1993, which was followed by six consecutive years without any publication
The last decade had witnessed the significant rise of interest in the applications of AI in depression studies and interventions
Summary
Depression is among the most common psychiatric conditions with a lifetime prevalence of10.8% [1]. Depression is characterized by low mood, loss of interest, low energy level, poor sleep, poor appetite, suicidal thought, and poor concentration [2]. Along with anxiety, these problems may result in chronic detrimental impairments and even lead to suicidal ideation [3], medical comorbidity [4,5,6], unhealthy lifestyles [7], and unproductivity [8]. Depressive disorders caused over 50 million Years Lived with Disability (YLD) worldwide, accounting for 7.5% of global total YLD, and are regarded as the largest contributor to non-fatal health loss [10]. AI plays a decisive role in the fourth industrial revolution, named
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