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This pilot study demonstrates the feasibility of deploying foundation models specifically trained for mental health, showing significant reductions in depression and anxiety symptoms alongside improved social interaction over a 12-month period. The research highlights critical implementation factors such as early working alliance and dynamic content scaling based on severity, while confirming that automated safety safeguards functioned appropriately without direct survey access. These findings provide essential empirical data on the real-world efficacy and operational constraints of generative AI in clinical settings.
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Generative AI Purpose-built for Social and Mental Health: A Real-World Pilot

arXiv2 min read

arXiv:2511.11689v4 Announce Type: replace-cross Abstract: 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.

Abstract: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.

Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2511.11689 [cs.CY]

(or arXiv:2511.11689v4 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2511.11689 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 248 words

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