Large language models show promise in supporting psychiatric interviews by analyzing conversation data to identify symptoms and create clinical summaries.

• Effectively identifies conversational segments indicating psychiatric symptoms
• Recognizes various mental health conditions through dialogue analysis
• Creates concise, clinically relevant summaries from patient interviews
• Tested specifically with North Korean defectors experiencing trauma

This innovation could significantly improve mental healthcare accessibility by helping practitioners identify symptoms more efficiently and maintain comprehensive records, particularly valuable in underserved communities and for patients with trauma.

Original Paper: Aligning Large Language Models for Enhancing Psychiatric Interviews Through Symptom Delineation and Summarization: Pilot Study