In a vignette study of 354 U.S. participants, deletion-based privacy controls outperformed all other controls in increasing willingness to engage with GenAI chatbots for emotional support, while technically complex controls like local-only processing and model training opt-outs reduced engagement.
Benefits and Harms of Large Language Models in Digital Mental Health
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
abstract
The past decade has been transformative for mental health research and practice. The ability to harness large repositories of data, whether from electronic health records (EHR), mobile devices, or social media, has revealed a potential for valuable insights into patient experiences, promising early, proactive interventions, as well as personalized treatment plans. Recent developments in generative artificial intelligence, particularly large language models (LLMs), show promise in leading digital mental health to uncharted territory. Patients are arriving at doctors' appointments with information sourced from chatbots, state-of-the-art LLMs are being incorporated in medical software and EHR systems, and chatbots from an ever-increasing number of startups promise to serve as AI companions, friends, and partners. This article presents contemporary perspectives on the opportunities and risks posed by LLMs in the design, development, and implementation of digital mental health tools. We adopt an ecological framework and draw on the affordances offered by LLMs to discuss four application areas -- care-seeking behaviors from individuals in need of care, community care provision, institutional and medical care provision, and larger care ecologies at the societal level. We engage in a thoughtful consideration of whether and how LLM-based technologies could or should be employed for enhancing mental health. The benefits and harms our article surfaces could serve to help shape future research, advocacy, and regulatory efforts focused on creating more responsible, user-friendly, equitable, and secure LLM-based tools for mental health treatment and intervention.
years
2026 6representative citing papers
LLMs prompted as peer supporters for ADRD caregivers produce synthetic lived experience through narrative language that differs from human peers in first-person and past-tense usage, revealing a narrative authenticity gap.
TEA Nets extracts agents, events, and targets from text to reveal emotional and semantic patterns in conspiracy theories and psychotherapy transcripts from humans and LLMs.
LLM safety guardrails fail for most mental health conditions with up to 100% failure rates for eating disorders, substance use disorder, and major depressive disorder, while holding only for suicide and self-harm.
Analyzing intermediate reasoning in LLMs reveals substantially more mental health stigma than MCQ evaluations by using clinical categories to tag and rate problematic statements.
An algorithm audit finds that OpenAI moderation, Llama Guard, and Shield Gemma frequently flag content from real therapy sessions as undesirable.
citing papers explorer
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The Impact of Security and Privacy Controls on Users' Emotional Engagement with Generative AI Chatbots
In a vignette study of 354 U.S. participants, deletion-based privacy controls outperformed all other controls in increasing willingness to engage with GenAI chatbots for emotional support, while technically complex controls like local-only processing and model training opt-outs reduced engagement.
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When AI Says "I have been in similar situations": Synthetic Lived Experience in Peer-Like Caregiver Support
LLMs prompted as peer supporters for ADRD caregivers produce synthetic lived experience through narrative language that differs from human peers in first-person and past-tense usage, revealing a narrative authenticity gap.
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The TEA Nets framework combines AI and cognitive network science to model targets, events and actors in text
TEA Nets extracts agents, events, and targets from text to reveal emotional and semantic patterns in conspiracy theories and psychotherapy transcripts from humans and LLMs.
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One Year Later...The Harms Persist, But So Do We!
LLM safety guardrails fail for most mental health conditions with up to 100% failure rates for eating disorders, substance use disorder, and major depressive disorder, while holding only for suicide and self-harm.
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Analyzing LLM Reasoning to Uncover Mental Health Stigma
Analyzing intermediate reasoning in LLMs reveals substantially more mental health stigma than MCQ evaluations by using clinical categories to tag and rate problematic statements.
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AI Content Moderation in Therapy Conversations
An algorithm audit finds that OpenAI moderation, Llama Guard, and Shield Gemma frequently flag content from real therapy sessions as undesirable.