This study evaluates the effectiveness of various AI approaches for detecting Post-Traumatic Stress Disorder from clinical interview transcripts, comparing traditional NLP with newer large language models.

• Tested multiple techniques including BERT/RoBERTa models, embedding-based methods, and different LLM prompting strategies
• Assessed performance using the DAIC-WOZ dataset of clinical interviews
• Identified which AI approaches most accurately detect PTSD markers in conversational data

This research has significant medical implications by potentially improving early PTSD detection in clinical settings, addressing the persistent challenge of underdiagnosis, and supporting more timely interventions for patients.

Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models