Digital Mental Health Interventions

Latest papers

• Right Words, Wrong Moment: A Clinician-Grounded Analysis of Distress in 19,930 Conversations between Young People and ChatGPT

  Marx Wang, Ella Zhang, Cameron Tan, Andrea Mock, Songling Ngo, Zijing Wang, Robert Wolfe, Shirin Amouei, Rachel A. Hanebutt, Desmond C. Ong, Caroline Figueroa, Katie Davis, Anind K. Dey, Alexis Hiniker · 30 September 2026

  Young people increasingly turn to General-Purpose Conversational Agents (GPCAs), such as ChatGPT, in moments of distress. We examine young adults' (ages 18-25) experiences using ChatGPT. We first collected 19,930 ChatGPT conversations and survey data from 158 young adults. We then selected five exam…

• MACBT: A Multi-Agent Cognitive Behavioral Therapy Decision Support System with Longitudinal Memory

  De Jiang, Shuo Zhang, Weiwei Liao, Jianying Zhang, Chuanhui Yu, Hongen Liao, Kehong Yuan · 28 September 2026

  Cognitive behavioral therapy (CBT) is an evidence-based first-line treatment for depression, yet its scale is constrained by the time clinicians spend on pre-session preparation, post-session documentation, and longitudinal cognitive-pathology tracking. We present a clinician-facing AI decision-supp…

• From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health

  He Hu, Yucheng Zhou, Qianning Wang, Yingjian Zou, Chiyuan Ma, Juzheng Si, Jianzhuang Liu, Zitong Yu, Laizhong Cui, Fei Ma, Qi Tian · 23 September 2026

  The rising global prevalence of mental health conditions, together with longstanding barriers in traditional healthcare, such as limited resources, high cost, stigma, and privacy concerns, has created an urgent need for accessible and scalable support. Large Language Models (LLMs) have emerged as a …

• Generative Artificial Intelligence Chatbots for Motivational Interviewing: A Scoping Review From System Design to Intervention Outcomes

  Runze Hu, Jingqi Kong, Yang Yang, Yihang Yang, Jingyao Liu, Haizhou Tang, Shanghang Zhang, Zheng Liu · 21 September 2026

  Motivational interviewing (MI) is a collaborative approach to elicit autonomous motivation for health behavior change. Generative AI (GenAI) offers new ways to deliver MI via conversational systems, but evidence on their design, assessment, and translation into interventions remains fragmented. This…

• From Momentary Emotion Inference to Sustained Emotion Support: Evaluating a Companion Agent in a Longitudinal Study

  Kexin Quan, Zijian Ding, Jiaye Yong, Qinshi Zhang, Dong Wang, Jessie Chin · 21 September 2026

  Sustained emotional support is a long-horizon interaction task closely tied to human well-being. Recent research demonstrates generative agents' capacity for momentary emotional support, yet how these capabilities sustain support over time remains unclear. To examine this challenge, we deployed PAIR…

• Clinician-Grounded Quality Assurance for AI-Assisted Psychiatric Intake

  King Shi, Amanda Li, Jonathan Ivey, Synthia Qia Wang, Guan Gui, Hyunseo Kim, Peter Zandi, Jason Straub, Jacob Taylor, Ananya Joshi · 21 September 2026

  Before patients can use AI-assisted psychiatric intake systems, health systems need practical ways to routinely evaluate these tools against their clinical standards for quality assurance. Because clinicians may use different intake styles, evaluation for this task must (1) support comparison across…

• Disrupted Companionship: A Risk Assessment Framework and Cross-Platform Quantitative Analysis of Psychosocial Responses to AI Companion Disruptions

  Chau Do, Yunhao Yuan, Koustuv Saha, Renwen Zhang, Talayeh Aledavood · 16 September 2026

  AI companions can provide meaningful relationships, yet these relationships remain vulnerable to platform-initiated changes. We study AI companion disruptions: platform changes that alter or terminate users' ongoing companionship with an AI. We compile 30 disruption events across major platforms, de…

• Effects of Static and Conversational AI-Generated Messages on Colorectal Cancer Screening Intent: a Randomized Clinical Trial

  Neil K. R. Sehgal, Manuel Tonneau, Andy SL Tan, Shivan J. Mehta, Alison Buttenheim, Lyle Ungar, Anish K. Agarwal, Sharath Chandra Guntuku · 15 September 2026

  Large language model chatbots show increasing promise in persuasive communication, but their clinical utility remains uncertain, particularly in settings where sustained conversations are difficult to scale. In a randomized clinical trial, 915 U.S. adults (ages 45-75) who had never completed colorec…

• PeerPen: AI-Assisted Writing for Online Mental Health Peer Support

  Jiwon Kim, Sherry Gong, Maya Ajit, Soorya Ram Shimgekar, Yunhao Yuan, Dong Whi Yoo, Eshwar Chandrasekharan, Koustuv Saha · 15 September 2026

  Online mental health communities thrive on peer support, yet those who volunteer to help often lack formal training and may struggle to articulate supportive responses. AI co-writing could lower this barrier; however, peer support derives much of its value from being perceived as personal, raising q…

• Vulnerabilities in Personalization: Assessing Health Privacy Risks in ChatGPT Logs and Memory

  S M Mehedi Zaman, Md Mozammel Hoque · 15 September 2026

  As conversational LLMs become deeply embedded in daily life, users frequently disclose sensitive personal health information during routine interactions. We present a large-scale computational audit analyzing 179,057 conversations across India, Nigeria, Brazil, and Pakistan (N = 1,057) to evaluate p…

• RESCUE-BENCH: Towards Relation-Aware Multi-Party Emotional Support Conversation Systems

  Haichuan Hu, Yang Xiao, Mingni Tang, Jiawen Duan, Quanjun Zhang, Congqing He, Hao Zhang, Jiashuo Wang, Johan F. Hoorn, Wenjie Li · 10 September 2026

  Existing emotional support conversation systems mainly focus on one-on-one seeker-supporter interactions and individual emotional states, leaving interpersonal relations in multi-party scenarios underexplored. In this work, we introduce relation-aware emotional support conversation, a new task that …

• Scalable Oversight for AI in Mental Health: Lessons from 350,000 AI Coaching Conversations between Therapy Sessions

  Matthew A. Scult, John L. Havlik, Kevin Ramotar, Ethan Goh, Manoj Kanagaraj · 10 September 2026

  Clinician review of every AI output is often proposed as a safeguard in mental healthcare, but vigilance research suggests this approach fails at scale and may paradoxically reduce safety. Drawing on our experience deploying an AI coaching tool across 350,000+ conversations between therapy sessions,…

• Delusions and Harms Associated with AI Chatbot Use: Early Evidence from 185 Real-World Reports

  Hamilton Morrin, Vinitha Soundararajan, Thomas Cheliotis-James, Boris Warszawski, Joshua Fakulujo, Zeqi Jia, Etienne Brisson, Thomas A. Pollak · 9 September 2026

  Importance: Reports have raised concerns that AI chatbots may validate or elaborate delusional beliefs, respond inappropriately to suicidal ideation, and contribute to mental health harms, but real-world data on reported harms remain limited. Objective: To characterize psychopathological features,…

• How a Chatbot's Response Style Shapes a Classroom: A Multi-Agent Simulation of Students Consulting AI

  Rin Tamai, Yuya Dan · 7 September 2026

  LLM-based chatbots are increasingly used as everyday confidants. Because they are designed to maximize user satisfaction, they can respond with excessive empathy and affirmation, which may reinforce mistaken beliefs and foster dependence on AI. While the psychological effects of chatbots on individu…

• Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors

  Vivian Nguyen, Lillian Lee, Elizabeth A. Olson, Cristian Danescu-Niculescu-Mizil · 7 September 2026

  How do people learn to become better conversationalists? This question is especially important in the context of mental-health counseling, where conversational skills are essential, yet volunteer counselors often have limited access to supervision and structured feedback. Understanding how counselor…

• When Retrieval Helps: Selective Retrieval for Single-Turn Mental-Health QA

  Hyunseo Oh, Chong-Kwon Kim, Yoonhyuk Choi · 4 September 2026

  Retrieval-augmented generation (RAG) can improve the specificity and grounding of large language model responses, but its effect is not uniformly beneficial in single-turn mental-health question answering, where user queries often combine emotional distress, treatment concerns, and safety-sensitive …

• How Does LGBTQIA+ Identity Affect LLM Behavior? Implications for Requirements Engineering of Mental Health AI Systems

  Shailyn Callihoo, Karman Singh, Navreet Dhillon, Harkiran Saini, Brody Stuart Verner, Ronnie de Souza Santos · 2 September 2026

  Large Language Models are now part of healthcare and mental health support systems, raising concerns regarding fairness toward vulnerable populations, including LGBTQIA+ individuals. However, limited empirical work has investigated how explicit LGBTQIA+ identity disclosure influences LLM-generated r…

• BALMS: Benchmarking Agentic LLMs for Longitudinal Mental Health Sensing

  Yu Yvonne Wu, Arvind Pillai, Yuliang Chen, Yuwei Zhang, Sudarshan Regmi, Tess Z. Griffin, Michael V. Heinz, Lisa A. Marsch, Nicholas C. Jacobson, Andrew Campbell · 28 August 2026

  Mental health assessment relies on episodic self-report scales, which convert subjective states such as stress into numerical scores but provide only sparse snapshots of wellbeing. Wearable devices offer longitudinal behavioral and physiological signals for continuous, low-burden monitoring. Recent …

• Beyond Reflection: Affirmation as a Promising Behavioral Marker Associated with Quality in Text-Based Counseling

  Michimasa Inaba · 28 August 2026

  While AI-assisted text-based counseling is gaining attention, it remains empirically unclear which counselor behaviors are associated with higher dialogue quality. Existing research often focuses heavily on Reflection, borrowing frameworks from Motivational Interviewing. To address this gap, we cond…

• A Safety-Gated Multimodal AI Backend for Mental-Health Support: Hierarchical State Representation, Conservative Risk Fusion, and Controlled Generation in Anian

  Lei Wang, Xiao Wang, Lei Li · 28 August 2026

  Safety-critical mental-health support systems must distinguish when supportive conversation is appropriate from when free-form generation should be blocked. This paper presents Anian, a safety-gated multimodal AI backend for perinatal mental-health support and mindfulness-intervention routing. Anian…

• An Echo Chamber of One: Should AI Psychosis Be a Distinct Clinical Entity?

  Joshua Au Yeung, Hamilton Morrin, Vincent Ng, Zeljko Kraljevic, Richard Dobson · 26 August 2026

  "AI psychosis" has entered public and clinical discourse as a label for the onset or exacerbation of psychotic symptoms, most commonly delusions, following intensive interaction with large language model (LLM)-based chatbots. Current evidence is limited to media reports, case reports, and early obse…

• CAIA in Practice: Field Evaluation of an AI-Assisted Support System for Text-Based Online Counselling

  Philipp Steigerwald, Nico Bienlein, Jennifer Burghardt, Mara Stieler, Robert Lehmann, Jens Albrecht · 25 August 2026

  Rising global demand for mental health support creates significant service delivery challenges, with asynchronous email counselling serving as a crucial low-threshold channel for accessing care. This paper presents CAIA, a co-designed AI-based tool suite that demonstrates responsible AI integration …

• Performance of a domain-specific large language model in answering patient questions in psychiatry

  Alexander J. Hish, Arjun Nagendran, Scott N. Compton · 25 August 2026

  Background This study was designed to evaluate whether a domain-specific large language model (LLM) trained exclusively on patient education resources can answer questions about psychiatric medications, in a manner superior to LLM chatbots. We developed an LLM ("MIND") fine-tuned for clinical fideli…

• DeepSAGE: Stage-Aware Reinforcement Learning for Structured CBT Counseling Dialogue

  Qi Zhang, Heajun An, Prakriti Dumaru, Sang Won Lee, Lifu Huang, Pamela J. Wisniewski, Jin-Hee Cho · 25 August 2026

  Large Language Model (LLM)-based counseling agents can generate fluent and supportive responses, but they often lack the structured, goal-directed progression required to conduct a coherent therapeutic session. We present DeepSAGE (Strategic AI Guidance Engine), a hybrid LLM--Deep Reinforcement Lear…

• ESCRAG-R1: Retrieval-Augmented Reinforcement Learning for Emotional Support Conversation

  Weichu Liu, Yuxuan Hu, Yirong Sun, Ningning Mao, Ziyun Zhang, Jian Chen, Mingyang Xu, Qishan Zhong, Chengming Li · 25 August 2026

  Emotional Support Conversation (ESC) systems aim to provide holistic support by balancing professional therapeutic competence with natural empathy. However, existing methods struggle to simultaneously achieve structured, stage-aware reasoning and seamless empathy-expertise alignment, often resulting…