AI-Augmented Psychosocial Interventions: A Bibliometric Review and Implications for Nursing.

Abstract

Purpose: To map out the current artificial intelligence (AI)-informed psychosocial interventions research landscape, with a focus on main themes, trends, and prospective future directions.

Method: A bibliometric analysis extracted articles that had been published between 2007 and 2024 from the Web of Science database. Software used to process results were Bibliometrix and VOSviewer.

Results: A total of 207 articles published by 86 different sources were obtained. A publication of high recurrence source was the Journal of Medical Internet Research. The United States showed high research activity in link strength, volume of articles, and citation frequency. Key themes identified were machine learning, mental health, cognitive-behavioral therapy, and personalization. Emerging trends since 2020 show growing interest in ChatGPT and AI-driven therapy.

Conclusion: Bibliometric analysis suggests increased application of AI in psychosocial interventions in mental health. Integrating AI with existing therapies and the development of novel digital tools indicate a future for mental health care that is personalized and innovative. The advent of advanced language models, such as ChatGPT, has opened new horizons in AI-supported mental health care. This preliminary analysis provides a foundational understanding of the current landscape while identifying key areas for further research. [Journal of Psychosocial Nursing and Mental Health Services, xx(xx), xx-xx.].