Development and evaluation of LLM-based suicide intervention chatbot - Cui, Xueting; Gu, Yun; Fang, Hui; Zhu, Tingshao (Frontiers in Psychiatry, 2025)
IntroductionSuicide accounts for over 720,000 deaths globally each year, and many more individuals experiencing suicidal ideation; thus, implementing large-scale, effective suicide intervention is vital for reducing suicidal behaviors. Traditional suicide intervention methods are hampered by shortages of qualified practitioners, variability in clinical competence, and high service costs. This study leverages Large Language Models (LLMs) to develop an effective suicide intervention chatbot, which provides early, large-scale, rapid self-help interventions.MethodsFirst, according to existing psychological crisis intervention methods, we fine-tuned ChatGPT-4 via prompt engineering to develop a chatbot that promptly responds to the needs of individuals experiencing suicidal ideation. Then, we implemented a self-help web-based dialogue platform powered by this chatbot and conducted the evaluations of its usability and intervention efficacy.ResultsWe found that the self-help suicide intervention chatbot achieved high effectiveness and quality in terms of user interface operability, interaction experience, emotional support, intervention efficacy, safety and privacy, and overall satisfaction.DiscussionThese findings demonstrate that the suicide intervention chatbot can provide effective emotional support and therapeutic intervention to a large cohort experiencing suicidal ideation.