A BIBLIOMETRIC REVIEW ON THE POWER OF ARTIFICIAL INTELLIGENCE IN DEPRESSION TREATMENT AND DETECTION

Abstract

In this systematic review we survey the playing field of artificial intelligence (AI) applications in depression detection and treatment from 1989 to September 2023, based on information retrieved from the Scopus database. This study contains multiple dimensions, in terms of publication distribution over years, leading authors, countries, affiliations, funding sponsors and co-occurring keywords. The results demonstrate the surge of publications in recent years due to the development of AI technologies. Authors such as U. Dannlowski and R.H. Perlis, and countries like the United States and China stand out as the most influential contributors. Affiliations such as Harvard Medical School and funding sources like the National Natural Science Foundation of China are important. Analysis of keywords highlights how ubiquitous words such as "machine learning" and "depression" are. Moreover, co-citation networks reveal influential authors, and the examination of highly cited papers provides an indication of research success. Recommendations for the future, focusing on the need for interdisciplinary collaboration, longitudinal research, field testing, and validation work, are described. Despite these important findings, the analysis was limited by the available data and the likelihood for bias in publication. All in all, this work offers a holistic summary of a shifting horizon at the AI and depression research crossway.