arXiv:2606.10380v1 Announce Type: new Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts. When applied to multi-turn dialogues, current models exhibit significant performance degradation, struggling to track risk signals that emerge as context evolves. To address this gap, we introduce CRADLE-Dialogue, a clinician-annotated benchmark for turn-level crisis detection in conversational settings. The dataset fea
The proliferation of conversational AI and increasing reliance on digital communication platforms highlight an urgent need for more sophisticated crisis detection in mental health.
This development allows for more accurate and timely intervention in mental health crises within digital conversational environments, potentially saving lives and improving care efficiency.
Current mental health crisis detection models are significantly improved by their ability to track evolving risk signals in multi-turn dialogues, rather than static texts.
- · Mental health platforms
- · Patients in crisis
- · AI developers in healthcare
- · Clinicians
- · Outdated static-text based detection models
- · Platforms without advanced crisis detection
Improved early intervention rates for mental health crises in online interactions.
Increased trust and adoption of AI-powered mental health support systems.
Potential for AI to augment, rather than simply automate, parts of the clinical diagnostic process for mental health conditions.
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