A Systematic Evaluation of Large Language Models for PTSD Severity Estimation: The Role of Contextual Knowledge and Modeling Strategies
arXiv:2602.06015v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly being used in a zero-shot (generative) fashion to assess mental health conditions, yet we have limited knowledge on what factors affect their accuracy. In this study, we use a clinical dataset of natural language narratives and self-reported PTSD severity scores from 1,437 individuals to comprehensively evaluate the performance of 11 state-of-the-art LLMs. To understand the factors affecting model's assessment accuracy, we systematically varied (i) contextual knowledge prompted to the models like
The proliferation of increasingly capable LLMs makes their application in sensitive domains like mental health assessment an active area of research and immediate concern.
This research provides a systematic understanding of how LLMs perform in mental health assessments, highlighting critical factors like contextual knowledge, which is crucial for responsible deployment and mitigating risks.
The understanding of LLM limitations and sensitivities in clinical applications, informing better models and safer deployment strategies for mental health support.
- · AI ethicists
- · Clinical researchers
- · Mental healthcare platforms
- · Healthcare AI developers
- · Unregulated AI mental health apps
- · Generative-only LLM approaches
- · Developers ignoring contextual factors
Improved accuracy and safety guidelines for LLM applications in mental health, potentially leading to more trust in AI-assisted diagnostics.
Formalization of best practices for integrating contextual knowledge and specific modeling strategies into AI systems for sensitive applications beyond mental health.
Enhanced regulatory frameworks specifically for AI in healthcare, focusing on explainability, bias mitigation, and patient safety, accelerating responsible AI adoption.
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