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Related Experiment Video

Updated: May 7, 2026

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TRUST: an large language model-based dialogue system for trauma understanding and structured assessments.

Sichang Tu1, Abigail Powers2, Stephen Doogan3

  • 1Department of Computer Science, Emory University, Atlanta, GA 30322, United States.

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Summary

This study introduces TRUST, a large language model (LLM) system for mental healthcare diagnostics. The LLM-powered dialogue system replicates clinician behavior for post-traumatic stress disorder (PTSD) assessments, improving accessibility.

Keywords:
clinical interviewdialogue systemlarge language modelmental healthpost-traumatic stress disorder

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Clinical Psychology
  • Natural Language Processing

Background:

  • Large language models (LLMs) are increasingly used in healthcare, but not for diagnostic interviews.
  • Mental healthcare accessibility remains a challenge.
  • Standardized diagnostic interviews are crucial for accurate assessment.

Purpose of the Study:

  • To develop an LLM-powered dialogue system (TRUST) that replicates clinician behavior for diagnostic interviews.
  • To bridge the gap in mental healthcare accessibility.
  • To conduct formal diagnostic assessments for post-traumatic stress disorder (PTSD).

Main Methods:

  • Introduced TRUST, a framework of cooperative LLM modules.
  • Utilized a Dialogue Acts schema for clinical interview response generation.
  • Developed a patient simulation approach using real-life interview transcripts for efficient testing.

Main Results:

  • Designed comprehensive evaluation metrics for agent and patient simulation perspectives.
  • Expert evaluations confirmed TRUST performs comparably to real-life clinical interviews.
  • Demonstrated clinical quality approaching that of human clinicians.

Conclusions:

  • The TRUST framework shows potential to significantly enhance mental healthcare availability.
  • The system's performance approaches clinical quality, with opportunities for future communication improvements.
  • LLM-powered dialogue systems can effectively support diagnostic processes.