Young people are experiencing worsening mental health and a growing reliance on online tools and services to address mental health difficulties. At the same time, next-generation large language models (LLMs) that are deployed through 'chatbot style interfaces', using deep learning artificial intelligence akin to interacting with a human appear to mark an opportunity for mental health therapeutics when designed specifically for clinical intervention. However, emergent evidence suggests the use of more generic LLM chatbots may pose a risk of providing misinformation, bias, or over reliance for some individuals when used outside of clinical contexts for mental health. This perspective paper examines the intersection of youth mental health and the rapid adoption of LLM chatbots. It first contextualises rising mental health challenges among young people alongside their increasing reliance on digital solutions. The paper then explores the potential benefits of LLM chatbot style interfaces in clinical mental health interventions. Following this, we discuss the evidence surrounding adverse mental health outcomes from the use of generic LLMs to support mental health at population level, describing complex system-level and human-level factors noted from the evidence. Finally, we outline considerations for public health and youth mental health discourse, purpose built LLM platform design, and a supporting research agenda. While current evidence on benefits and risks from generic LLMs is emergent and not youth-specific, this perspective highlights a need for research focused on young people to ensure safe and effective use of widely available LLMs for mental health support.
Matthews et al. (Fri,) studied this question.