Generative AI Purpose-built for Social and Mental Health: A Real-World Pilot
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Abstract (arXiv)
Generative AI chatbots built for mental health could extend access to care, but evidence from real-world use is limited. We report a single-arm, naturalistic pilot of a foundation model trained for mental health, among 299 US adults with at least moderate depressive or anxiety symptoms who were followed for up to 12 months. Depression and anxiety symptoms fell by 10 weeks (Cohen's d 0.93 and 0.79), with loneliness, behavioral activation, and social interaction improving. Clinicians confirmed that automated safeguards were escalated appropriately. Participants fell into non-responding (57.2%), improving (37.1%) and rapidly improving (5.7%) trajectories. Early working alliance and greater engagement were associated with better outcomes. The AI deployed ten identified intervention families whose delivery varied with baseline anxiety and depression, with the overall ratio of clinical to non-clinical content increasing according to severity without survey information access. These findings support the feasibility of purpose-built AI for mental health.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 57 references that are in the map. First posted on arXiv 12 Nov 2025.
Builds on
4 of 57 references.
References · 4
- 2024Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooMcited by 78
- 2025Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providerscited by 69
- 2024TnT-LLM: Text Mining at Scale with Large Language Modelscited by 26
- 2024Parsimoniously Fitting Large Multivariate Random Effects in glmmTMBcited by 1
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Citing papers per year: 2026: 1.
1 paper.