Mental Health Generative AI is Safe, Promotes Social Health, and Reduces Depression and Anxiety: Real World Evidence from a Naturalistic Cohort

• URL: http://arxiv.org/abs/2511.11689v1
• Date: Wed, 12 Nov 2025 09:16:20 GMT
• Title: Mental Health Generative AI is Safe, Promotes Social Health, and Reduces Depression and Anxiety: Real World Evidence from a Naturalistic Cohort
• Authors: Thomas D. Hull, Lizhe Zhang, Patricia A. Arean, Matteo Malgaroli,
• Abstract summary: Generative artificial intelligence (GAI) built for mental health could deliver safe, personalized, and scalable mental health support.<n>We evaluate a foundation model designed for mental health.
• Score: 0.3499870393443268
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative artificial intelligence (GAI) chatbots built for mental health could deliver safe, personalized, and scalable mental health support. We evaluate a foundation model designed for mental health. Adults completed mental health measures while engaging with the chatbot between May 15, 2025 and September 15, 2025. Users completed an opt-in consent, demographic information, mental health symptoms, social connection, and self-identified goals. Measures were repeated every two weeks up to 6 weeks, and a final follow-up at 10 weeks. Analyses included effect sizes, and growth mixture models to identify participant groups and their characteristic engagement, severity, and demographic factors. Users demonstrated significant reductions in PHQ-9 and GAD-7 that were sustained at follow-up. Significant improvements in Hope, Behavioral Activation, Social Interaction, Loneliness, and Perceived Social Support were observed throughout and maintained at 10 week follow-up. Engagement was high and predicted outcomes. Working alliance was comparable to traditional care and predicted outcomes. Automated safety guardrails functioned as designed, with 76 sessions flagged for risk and all handled according to escalation policies. This single arm naturalistic observational study provides initial evidence that a GAI foundation model for mental health can deliver accessible, engaging, effective, and safe mental health support. These results lend support to findings from early randomized designs and offer promise for future study of mental health GAI in real world settings.

Related papers

• Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data [2.2310516973117194]
  This study evaluated the feasibility of integrating active and passive smartphone data to predict mental disorders in non-clinical adolescents.<n>We investigated the Mindcraft app in predicting risks for internalising and externalising disorders, eating disorders, insomnia and suicidal ideation.
  arXiv  Detail & Related papers  (2025-01-15T15:05:49Z)
• MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
  Mental health disorders are one of the most serious diseases in the world.<n>Privacy concerns limit the accessibility of personalized treatment data.<n>MentalArena is a self-play framework to train language models.
  arXiv  Detail & Related papers  (2024-10-09T13:06:40Z)
• IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being [8.437366120438156]
  Digital technologies can support Sustainable Development Goals 3. "Good Health and Well-Being" ensures healthy lives and promotes well-being for all ages. burnout and depression could be reduced by encouraging better preventive health.
  arXiv  Detail & Related papers  (2024-06-19T19:35:14Z)
• Predicting Depression and Anxiety: A Multi-Layer Perceptron for Analyzing the Mental Health Impact of COVID-19 [1.9809980686152868]
  We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends during the COVID-19 pandemic. Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults. This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health.
  arXiv  Detail & Related papers  (2024-03-09T22:49:04Z)
• PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
  Psychological measurement is essential for mental health, self-understanding, and personal development. PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
  arXiv  Detail & Related papers  (2024-02-19T18:00:30Z)
• Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs [77.88043871260466]
  We show that one of today's largest language models lacks this kind of social intelligence out-of-the box. We conclude that person-centric NLP approaches might be more effective towards neural Theory of Mind.
  arXiv  Detail & Related papers  (2022-10-24T14:58:58Z)
• Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [55.653944436488786]
  According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD. This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2022-07-03T11:33:52Z)
• Capturing social media expressions during the COVID-19 pandemic in Argentina and forecasting mental health and emotions [0.802904964931021]
  We forecast mental health conditions and emotions of a given population during the COVID-19 pandemic in Argentina based on language expressions used in social media. Mental health conditions and emotions are captured via markers, which link social media contents with lexicons.
  arXiv  Detail & Related papers  (2021-01-12T15:15:31Z)
• Personal Mental Health Navigator: Harnessing the Power of Data, Personal Models, and Health Cybernetics to Promote Psychological Well-being [2.491393414479041]
  We present the notion of Personalized Mental Health Navigation (MHN) MHN deploys a continuous cyclic loop of measurement, estimation, guidance, to steer the individual's mental health state towards a healthy zone. We demonstrate the feasibility of the personalized MHN approach via a 12-month pilot case study for holistic stress management in college students.
  arXiv  Detail & Related papers  (2020-12-15T18:34:09Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)
• A Robotic Positive Psychology Coach to Improve College Students' Wellbeing [16.70932067272569]
  We investigate the use of a social robot coach to deliver positive psychology interventions to college students living in on-campus dormitories. We found a statistically significant improvement in participants' psychological wellbeing, mood, and readiness to change behavior for improved wellbeing after they completed the study. Students' personality traits were found to have a significant association with intervention efficacy.
  arXiv  Detail & Related papers  (2020-09-08T15:51:11Z)
• Anxiety Detection Leveraging Mobile Passive Sensing [53.11661460916551]
  Anxiety disorders are the most common class of psychiatric problems affecting both children and adults. Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods. eWellness is an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner.
  arXiv  Detail & Related papers  (2020-08-09T20:22:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.