Decoding the cry for help: AI's emerging role in suicide risk assessment

Abstract

Artificial Intelligence (AI) has shown significant potential in identifying early warning signs of suicide, a critical global health issue claiming nearly 800,000 lives annually. This study examines how AI technologies—with a primary focus on conversational agents (chatbots), Natural Language Processing (NLP), deep learning, and Large Language Models (LLMs)—can enhance early detection of suicide risk through linguistic pattern analysis and multimodal approaches. Traditional suicide risk assessment methods often lack timely intervention capabilities due to limitations in scalability and continuous monitoring. We synthesize current research on AI-driven approaches for suicide risk detection, specifically examining (1) how NLP and deep learning techniques identify subtle linguistic patterns associated with suicidal ideation, (2) the emerging capabilities of LLMs in powering more contextually aware chatbot interactions, (3) ethical frameworks necessary for responsible clinical implementation, and (4) regulatory frameworks for suicide prevention chatbots. Our analysis reveals that AI-powered chatbots demonstrate improved accuracy in detecting suicidal ideation while providing scalable, accessible support. Additionally, we offer a comparative analysis of leading AI chatbots for mental health support, examining their therapeutic approaches, technical architectures, and clinical evidence to highlight current best practices in the field. We also present a novel framework for evaluating chatbot effectiveness in suicide prevention that offers standardized metrics across five key dimensions: clinical risk detection, user engagement, intervention delivery, safety monitoring, and implementation success. While AI chatbots provide significant potential to transform early intervention, substantial challenges remain in addressing conversation design, ensuring appropriate escalation protocols, and integrating these tools into clinical workflows. This paper examines the most promising chatbot approaches for suicide prevention while establishing concrete benchmarks for responsible implementation in clinical settings.