TRUST: An LLM-Based Dialogue System for Trauma Understanding and Structured Assessments

• URL: http://arxiv.org/abs/2504.21851v1
• Date: Wed, 30 Apr 2025 17:58:06 GMT
• Title: TRUST: An LLM-Based Dialogue System for Trauma Understanding and Structured Assessments
• Authors: Sichang Tu, Abigail Powers, Stephen Doogan, Jinho D. Choi,
• Abstract summary: This study aims to bridge the gap in mental healthcare accessibility by developing an LLM-powered dialogue system that replicates clinician behavior.<n>We introduce TRUST, a framework of cooperative LLM modules capable of conducting formal diagnostic interviews and assessments for PTSD.<n>We develop a patient simulation approach based on real-life interview transcripts to replace time-consuming and costly manual testing by clinicians.
• Score: 8.618945530676614
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Objectives: While Large Language Models (LLMs) have been widely used to assist clinicians and support patients, no existing work has explored dialogue systems for standard diagnostic interviews and assessments. This study aims to bridge the gap in mental healthcare accessibility by developing an LLM-powered dialogue system that replicates clinician behavior. Materials and Methods: We introduce TRUST, a framework of cooperative LLM modules capable of conducting formal diagnostic interviews and assessments for Post-Traumatic Stress Disorder (PTSD). To guide the generation of appropriate clinical responses, we propose a Dialogue Acts schema specifically designed for clinical interviews. Additionally, we develop a patient simulation approach based on real-life interview transcripts to replace time-consuming and costly manual testing by clinicians. Results: A comprehensive set of evaluation metrics is designed to assess the dialogue system from both the agent and patient simulation perspectives. Expert evaluations by conversation and clinical specialists show that TRUST performs comparably to real-life clinical interviews. Discussion: Our system performs at the level of average clinicians, with room for future enhancements in communication styles and response appropriateness. Conclusions: Our TRUST framework shows its potential to facilitate mental healthcare availability.

Related papers

• CliniChat: A Multi-Source Knowledge-Driven Framework for Clinical Interview Dialogue Reconstruction and Evaluation [20.64206737886915]
  We propose CliniChat, a framework that integrates multi-source knowledge to enable large language models to simulate real-world clinical interviews.<n>Two modules: Clini-Recon and Clini-Eval, each responsible for reconstructing and evaluating interview dialogues.<n>We contribute MedQA-Dialog, a high-quality synthetic interview dialogue dataset, and CliniChatGLM, a model specialized for clinical interviews.
  arXiv  Detail & Related papers  (2025-04-14T17:06:47Z)
• Large Language Models for Outpatient Referral: Problem Definition, Benchmarking and Challenges [34.10494503049667]
  Large language models (LLMs) are increasingly applied to outpatient referral tasks across healthcare systems.<n>There is a lack of standardized evaluation criteria to assess their effectiveness.<n>We propose a comprehensive evaluation framework specifically designed for such systems.
  arXiv  Detail & Related papers  (2025-03-11T11:05:42Z)
• ProAI: Proactive Multi-Agent Conversational AI with Structured Knowledge Base for Psychiatric Diagnosis [10.8749978349074]
  Most conversational AI systems operate reactively, responding to user prompts without guiding the interaction.<n>We introduce ProAI, a goal-oriented, proactive conversational AI framework.<n>We demonstrate that ProAI achieves up to 83.3% accuracy in mental disorder differential diagnosis.
  arXiv  Detail & Related papers  (2025-02-28T03:45:39Z)
• Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations [74.83732294523402]
  We introduce a novel benchmark that simulates real-world diagnostic scenarios, integrating noise and difficulty levels aligned with USMLE standards.<n>We also explore dialogue-based fine-tuning, which transforms static datasets into conversational formats to better capture iterative reasoning processes.<n>Experiments show that dialogue-tuned models outperform traditional methods, with improvements of $9.64%$ in multi-round reasoning scenarios and $6.18%$ in accuracy in a noisy environment.
  arXiv  Detail & Related papers  (2025-01-29T18:58:48Z)
• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment. We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews. Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• PALLM: Evaluating and Enhancing PALLiative Care Conversations with Large Language Models [10.258261180305439]
  Large language models (LLMs) offer a new approach to assessing complex communication metrics. LLMs offer the potential to advance the field through integration into passive sensing and just-in-time intervention systems. This study explores LLMs as evaluators of palliative care communication quality, leveraging their linguistic, in-context learning, and reasoning capabilities.
  arXiv  Detail & Related papers  (2024-09-23T16:39:12Z)
• LLM Questionnaire Completion for Automatic Psychiatric Assessment [49.1574468325115]
  We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C)
  arXiv  Detail & Related papers  (2024-06-09T09:03:11Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)
• Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration [72.04629217161656]
  This work focuses on three aspects of proactive dialogue systems: clarification, target-guided, and non-collaborative dialogues. To trigger the proactivity of LLMs, we propose the Proactive Chain-of-Thought prompting scheme.
  arXiv  Detail & Related papers  (2023-05-23T02:49:35Z)
• PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learning [20.437165038293426]
  The patient-centered medical dialogue systems strive to offer diagnostic interpretation services to users who are less knowledgeable about medical knowledge. It is difficult for the large language models (LLMs) to guarantee the specificity of responses in spite of its promising performance. Inspired by in-context learning, we propose PlugMed, a Plug-and-Play Medical Dialogue System.
  arXiv  Detail & Related papers  (2023-05-19T08:18:24Z)

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.