The rapid deployment of AI-powered mental health chatbots is outpacing safety research, creating a potential blind spot in digital therapeutics just as these tools gain widespread adoption. While millions seek mental health support through AI interfaces, the unpredictable nature of generative AI systems may pose unrecognized risks to vulnerable users.
This comprehensive review protocol aims to map the current landscape of generative AI mental health chatbots, specifically examining user experience outcomes and safety protocols. The researchers will systematically analyze existing literature across major databases including PubMed, Scopus, and specialized computer science repositories to identify patterns in chatbot development, implementation challenges, and documented adverse events. The focus extends beyond efficacy to examine risk mitigation strategies and user safety frameworks currently employed by developers.
The timing reflects growing clinical concerns about AI chatbots operating without the safety guardrails typical of traditional mental health interventions. Unlike conventional digital mental health tools with scripted responses, generative AI systems can produce unpredictable outputs that may inadvertently harm users in crisis situations. This represents a significant shift from earlier chatbot generations that followed predetermined conversation trees.
The review's systematic approach may reveal critical gaps in current safety protocols and highlight the need for standardized risk assessment frameworks. Given the scalability advantages of AI mental health tools in addressing global treatment gaps, establishing robust safety standards becomes essential for responsible deployment. The findings could inform regulatory approaches and clinical guidelines for integrating AI chatbots into mental health care systems.
