Unleashing the potential of chatbots in mental health: bibliometric analysis.

Background: The proliferation of chatbots in the digital mental health sector is gaining momentum, offering a promising solution to address the pressing shortage of mental health professionals. By providing accessible and convenient mental health services and support, chatbots are poised to become a primary technological intervention in bridging the gap between mental health needs and available resources. Objective: This study undertakes a thorough bibliometric analysis and discourse on the applications of chatbots in mental health, with the objective of elucidating the underlying scientific patterns that emerge at the intersection of chatbot technology and mental health care on a global scale. Methods: The bibliometric software Biblioshiny and VOSviewer were used to conduct a comprehensive analysis of 261 scientific articles published in the Web of Science Core Collection between 2015 and 2024. Publications distribution are analyzed to measure productivity on countries, institutions, and sources. Scientific collaboration networks are generated to analyze the influence as well as communications between countries and institutions. Research topics and trends are formulated by using a keyword co-occurrence network. Results: Over the last decade, researches on utilization of chatbots in mental health has appeared to be increasing steadily at an annual rate of 46.19%. The United States have made significant contributions to the development and expansion of publications, accounting for 27.97% of the total research output with 2452 citation counts. England came second to the US in terms of publications and citations, and followed by Australia, China, and France. National Center for Scientific Research in France ranked first among all institutions, followed by Imperial College London and University of Zurich. The number of articles published in Journal of Medical Internet Research was exceptionally high, accounting for 12.26% of the total number of articles, and JMIR Mental Health is the most influential publication sources in terms of average citations per article. Collaboration among universities in the USA, United Kingdom, Switzerland, and Singapore demonstrated a high level. The keyword co-occurrence network highlights the prominent techniques in this multidisciplinary area and reveals 5 research topics, showing a significant overlap between clusters. High-frequency terms such as "ChatGPT", "machine learning", and "large language models" underscore the current state of research, highlighting the cutting-edge advancements and frontiers in this field. Conclusions: This study provides an in-depth analysis of the most prominent countries, institutions, publications, collaboration status, and research topics associated with utilization of chatbots in mental health over the last decade. It offers insights to mental health professionals without an AI background and individuals interested in the development of mental health chatbots. The findings suggest that chatbots hold a significant role in promoting mental health well-being and exhibit considerable potential in demonstrating empathy, curiosity, understanding, and collaborative capabilities with users.