User experience and safety of generative AI-based mental health chatbots: Scoping review protocol - Lotenna Olisaeloka; Chris Richardson; Daniel Vigo (PLoS ONE, 2026)
Introduction Mental health problems constitute a significant global health challenge due to their rising prevalence and substantial treatment gap. Digital Mental Health Interventions (DMHIs) including mental health chatbots have emerged as promising solutions due to their effectiveness and scalability. Recent advances in Generative Artificial Intelligence (GenAI) have improved the conversational abilities of these chatbots, further amplifying their potential. However, despite instances of inadvertent harm stemming from the unpredictable nature of GenAI, little attention has been paid to user experience and safety of these chatbots. Objective This proposed review will explore existing research on GenAI-based mental health chatbots. Specifically, it aims to identify and describe current chatbots, focusing on user experience, safety and risk mitigation strategies. Methods The review will follow the Joanna Briggs Institute (JBI) guidelines for conducting scoping reviews. It will also adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review (PRISMA-ScR). A systematic database search of Medline (PubMed), Scopus, PsycINFO, ACM Digital Library, and IEEE Xplore will be conducted. The database search will be complimented by research-based search engines (Google Scholar and Consensus). Studies focusing on the development, evaluation or implementation of GenAI-based mental health chatbots will be included without limitations to specific disorders or population groups. Two independent reviewers will perform screening and data extraction. The analysis will include descriptive summary and thematic analysis, with results presented in tabular, graphical, and narrative formats. Conclusion This review will provide a comprehensive overview of GenAI-based mental health chatbots while identifying innovative practices and knowledge gaps relating to user experience and safety. Findings will inform the ethical development, evaluation and implementation of GenAI-based mental health interventions.