aceHuman-in-the-Loop Suicide Risk Detection and Clinical Escalation in a Generative AI Mental Health Chatbot: Prospective Implementation Study
Background Mental illness is the leading cause of disability worldwide, affecting more than one billion individuals, and suicide accounts for 720,000 to 800,000 deaths annually. While AI-native mental health chatbots have the potential to fill a sizable gap between treatment need and workforce capacity, their rapid proliferation has outpaced the development of scalable procedures to identify, detect, and respond to suicidal ideation. Despite AI best practice recommendations to incorporate human oversight for saf…
A human-in-the-loop protocol combining universal screening and conversational detection was operationally feasible, starting clinician outreach about 11 minutes after risk alerts in a chatbot trial.
Clinicians successfully contacted only 30.9% of alerted participants in Phase 1 and 42.4% in Phase 2, leaving many flagged users without completed human assessment.
Contact and clinical assessment were not completed for a sizable share of alerted participants, and the effect of these procedures on clinical outcomes was not characterized.
Evidence
- Peer-reviewedJMIR Formative Research2026-10-09
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Truvace Impact Record TRV-2026-1681, v1: “Human-in-the-Loop Suicide Risk Detection and Clinical Escalation in a Generative AI Mental Health Chatbot: Prospective Implementation Study.” Truvace, 2026-10-17. /record/TRV-2026-1681 (accessed at citation time). sha256 38a7154c24047d38…
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