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Tell Me: An LLM-powered Mental Well-being Assistant with RAG, Synthetic Dialogue Generation, and Agentic Planning [0.0]
Tell Me is a mental well-being system that leverages advances in large language models to provide accessible, context-aware support for users and researchers.<n>The system integrates three components: (i) a retrieval-augmented generation (RAG) assistant for personalized, knowledge-grounded dialogue; (ii) a synthetic client-therapist dialogue generator conditioned on client profiles to facilitate research on therapeutic language and data augmentation; and (iii) a Well-being AI crew, implemented with CrewAI, that produces weekly self-care plans and guided meditation audio.
arXiv Detail & Related papers (2025-11-18T12:43:04Z)
Context-Emotion Aware Therapeutic Dialogue Generation: A Multi-component Reinforcement Learning Approach to Language Models for Mental Health Support [3.857814030650221]
Mental health illness represents a substantial global socioeconomic burden.<n>This paper investigated the application of supervised fine-tuning and reinforcement learning techniques to enhance GPT-2's capacity for therapeutic dialogue generation.
arXiv Detail & Related papers (2025-11-14T21:32:10Z)
MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions [58.61680631581921]
We introduce MAGneT, a novel multi-agent framework for synthetic psychological counseling session generation.<n>Unlike prior single-agent approaches, MAGneT better captures the structure and nuance of real counseling.<n> Empirical results show that MAGneT significantly outperforms existing methods in quality, diversity, and therapeutic alignment of the generated counseling sessions.
arXiv Detail & Related papers (2025-09-04T12:59:24Z)
Mentalic Net: Development of RAG-based Conversational AI and Evaluation Framework for Mental Health Support [0.0]
Mentalic Net Conversational AI has a BERT Score of 0.898, with other evaluation metrics falling within satisfactory ranges.<n>We advocate for a human-in-the-loop approach and a long-term, responsible strategy in developing such transformative technologies.
arXiv Detail & Related papers (2025-08-27T03:44:56Z)
Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models [92.93521294357058]
Narrative therapy helps individuals transform problematic life stories into empowering alternatives.<n>Current approaches lack realism in specialized psychotherapy and fail to capture therapeutic progression over time.<n>Int (Interactive Narrative Therapist) simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate expert-like responses.
arXiv Detail & Related papers (2025-07-27T11:52:09Z)
Spiritual-LLM : Gita Inspired Mental Health Therapy In the Era of LLMs [32.30725598459055]
This study introduces a novel framework by integrating spiritual wisdom from the Bhagavad Gita with advanced large language model GPT-4o.<n>We benchmark GITes against 12 state-of-the-art LLMs, including both mental health specific and general purpose models.<n>Our findings indicate a strong potential for AI systems enriched with spiritual guidance to enhance user satisfaction and perceived support outcomes.
arXiv Detail & Related papers (2025-06-23T23:02:57Z)
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning [50.94508930739623]
Medical visual question answering aims to support clinical decision-making by enabling models to answer natural language questions based on medical images.<n>Current methods still suffer from limited answer reliability and poor interpretability, impairing the ability of clinicians and patients to understand and trust model-generated answers.<n>This work first proposes a Thinking with Visual Grounding dataset wherein the answer generation is decomposed into intermediate reasoning steps.<n>We introduce a novel verifiable reward mechanism for reinforcement learning to guide post-training, improving the alignment between the model's reasoning process and its final answer.
arXiv Detail & Related papers (2025-06-22T08:09:58Z)
MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis [58.67342568632529]
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arXiv Detail & Related papers (2025-06-04T09:18:25Z)
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arXiv Detail & Related papers (2025-04-14T17:37:32Z)
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arXiv Detail & Related papers (2025-03-13T20:25:10Z)
AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral Therapy in Psychological Counseling [57.054489290192535]
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arXiv Detail & Related papers (2025-01-16T09:57:12Z)
Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment [66.6041949490137]
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arXiv Detail & Related papers (2024-11-17T00:13:00Z)
SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques [9.146311285410631]
Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources. This study aims to provide diverse, accessible, stigma-free, personalized, and real-time mental health support through cutting-edge AI technologies.
arXiv Detail & Related papers (2024-10-17T22:04:32Z)
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. Privacy concerns limit the accessibility of personalized treatment data. MentalArena is a self-play framework to train language models.
arXiv Detail & Related papers (2024-10-09T13:06:40Z)
Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots [0.0]
This paper explores the potential of AI-enabled chatbots as a scalable solution. We assess their ability to deliver empathetic, meaningful responses in mental health contexts. We propose a federated learning framework that ensures data privacy, reduces bias, and integrates continuous validation from clinicians to enhance response quality.
arXiv Detail & Related papers (2024-09-17T20:49:13Z)
Bridging the Skills Gap: Evaluating an AI-Assisted Provider Platform to Support Care Providers with Empathetic Delivery of Protocolized Therapy [5.6317091933490495]
Despite high prevalence and burden of mental health conditions, there is a global shortage of mental health providers. We developed AI-Assisted Provider Platform (A2P2), a text-based virtual therapy interface that includes a response feature. We studied providers with and without expertise in mental health treatment delivering a therapy session using the platform with (intervention) and without (control) AI-assistance features.
arXiv Detail & Related papers (2024-01-08T02:23:17Z)
Design, Development, and Evaluation of an Interactive Personalized Social Robot to Monitor and Coach Post-Stroke Rehabilitation Exercises [68.37238218842089]
We develop an interactive social robot exercise coaching system for personalized rehabilitation. This system integrates a neural network model with a rule-based model to automatically monitor and assess patients' rehabilitation exercises. Our system can adapt to new participants and achieved 0.81 average performance to assess their exercises, which is comparable to the experts' agreement level.
arXiv Detail & Related papers (2023-05-12T17:37:04Z)
Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
arXiv Detail & Related papers (2022-05-19T17:34:18Z)
Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy: Iterative Design and Evaluation with Therapists and Post-Stroke Survivors [66.07833535962762]
Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction. Previous work explored the potential of automatically monitoring exercises for AI and robotic coaches, but deployment remains a challenge. We present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patient's exercises.
arXiv Detail & Related papers (2021-06-15T22:06:39Z)
"Am I A Good Therapist?" Automated Evaluation Of Psychotherapy Skills Using Speech And Language Technologies [38.726068038788384]
We describe our platform and its performance, using a dataset of more than 5,000 recordings. Our system gives comprehensive feedback to the therapist, including information about the dynamics of the session. We are confident that a widespread use of automated psychotherapy rating tools in the near future will augment experts' capabilities.
arXiv Detail & Related papers (2021-02-22T18:52:52Z)
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)

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