Explainable AI for suicide risk detection: gender- and age-specific patterns from real-time crisis chats

This study demonstrates how explainable, theory-informed NLP can capture dynamic SR factors in real-world crisis interactions. Findings reveal distinct gender- and age-specific pathways, underscoring the need for personalized prevention strategies. Beyond theoretical insights, the approach highlights the potential of AI-driven, interpretable monitoring tools to support crisis counselors in detecting escalating risk earlier and tailoring interventions. Such methods can enhance the accuracy, timeliness, and equity of suicide prevention, and illustrate how explainable AI can translate psychological theory into clinically meaningful tools for mental health screening and early intervention.