ArticleNPJ digital medicine2026
An AI-based mental health guardrail and dataset for identifying psychiatric crises in text-based conversations.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Preparing AI chatbots to respond to patient distress and suicidality in high-risk healthcare settings.NPJ digital medicine · 2026Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models often mishandle psychiatric emergencies, offering harmful or inappropriate advice. This study evaluated the Verily Mental Health Guardrail (VMHG) on two clinician-labeled datasets: the Verily Mental Health Crisis Dataset v1.0, containing 1800 simulated messages and the NVIDIA Aegis AI Content Safety Dataset subsetted to 794 mental health-related messages. Performance was benchmarked against OpenAI Omni Moderation Latest and NVIDIA NeMo Guardrails. The VMHG demonstrated high sensitivity (0.990) and specificity (0.992) on the Verily dataset, with an F1-score of 0.939 and high category-level sensitivity (0.917-0.992) and specificity (≥0.978). On the NVIDIA dataset, it maintained strong sensitivity (0.982) and accuracy (0.921) with reduced specificity (0.859). Compared with NVIDIA and OpenAI guardrails, the VMHG achieved significantly higher sensitivity (all p < 0.001) and comparable specificity (NVIDIA p < 0.001, OpenAI p = 0.094). Overall, the VMHG demonstrated robust, generalizable, and clinically oriented safety performance that prioritizes sensitivity to minimize missed mental health crises.
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Registered trials
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