The paper reviews how large language models are applied in mental health, covering areas such as social media analysis, clinical conversational agents, therapy support tools, prompt engineering, and multimodal learning. It synthesizes interdisciplinary studies that use social media posts, electronic medical records, and multimodal inputs to detect depression, assess suicide risk, provide personalized therapy, and generate psychoeducational content. The review also discusses advances in model interpretability, annotation strategies, multimodal fusion techniques, and highlights ethical, sociotechnical, and regulatory challenges while proposing frameworks for safe, equitable, and accountable deployment.
By Yisong Chen, Yifan Gao, Sijing Yu, Chuqing Zhao, Yang Lu
The paper evaluates the safety of conversational AI therapy bots for Generation Alpha, revealing that while these models understand 76‑82% of youth‑specific vocabulary, they correctly assess clinical risk only 64‑72% of the time, creating a significant vocabulary‑comprehension gap. Six failure patterns—such as sarcasm masking, minimization acceptance, and semantic drift—were identified, with compounded errors leading to a 94% miss rate when three or more patterns co‑occur. The authors estimate 146,880 missed crises annually and recommend mandatory human‑in‑the‑loop systems, quarterly youth‑specific validation, transparent performance disclosure, and regulatory oversight for youth‑facing mental health AI.
By Manisha Mehta, Virendra Mehta
BiGraph-Diffuse is a large‑scale diffusion language model designed for mental health counseling, addressing two key limitations of existing AI dialogue systems: the lack of bidirectional understanding for progressive disclosure and the inadequate use of relational clinical knowledge. It pairs this diffusion model with BiGraph‑RAG, a graph‑structured retrieval approach that uses lightweight entity extraction and semantic linking to preserve inferential pathways from symptoms to underlying causes without incurring LLM token costs during indexing. Experiments and theoretical analysis demonstrate the effectiveness of this mutually reinforcing architecture.
By Yuxiang Cheng, Quanwei Tang, Lvhui Lu, Dong Zhang, Shoushan Li, Erik Cambria
arXiv:2510.25384v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are promising tools for synthetic data generation in mental health. However, privacy policies and restrictions forced...
By Doan Nam Long Vu, Rui Tan, Lena Moench, Svenja Jule Francke, Daniel Woiwod, Florian Thomas-Odenthal, Sanna Stroth, Tilo Kircher, Christiane Hermann, Udo Dannlowski, Hamidreza Jamalabadi, Simone Balloccu, Shaoxiong Ji
arXiv:2607. 10871v1 Announce Type: new Abstract: Contemplative traditions have long guided ethical behavior and prosocial interaction, and recent work suggests that contemplative principles (e.
By Asher Sprigler, Yang-Yang Feng, Iftach Amir, Jonathan E. Bogard, Todd S Braver, Yi Ding, David Kinney, Yixue Zhao
Graph2Counsel is a framework that generates synthetic counseling dialogues by leveraging Client Psychological Graphs (CPGs) to encode the relationships among a client’s thoughts, emotions, and behaviors. The system uses a structured prompting pipeline guided by counselor strategies and explores techniques such as Chain‑of‑Thought and Multi‑Agent Feedback to produce 760 realistic sessions from 76 CPGs. Expert evaluation shows the dataset surpasses previous ones in specificity, counselor competence, authenticity, conversational flow, and safety, and fine‑tuning an open‑source model on it improves performance on several counseling benchmarks.
By Aishik Mandal, Hiba Arnaout, Clarissa W. Ong, Juliet Bockhorst, Kate Sheehan, Rachael Moldow, Tanmoy Chakraborty, Iryna Gurevych