This research demonstrates how Large Language Models can be leveraged to quantify mental states objectively, creating new possibilities for psychiatric assessment tools.

• Utilizes self-report questionnaires processed by LLMs to detect patterns in emotional responses
• Draws parallels between human emotional states and LLM response consistency
• Establishes a framework that could revolutionize mental health screening
• Offers potential for more accessible, standardized psychiatric evaluations

This innovation matters for healthcare because it creates a scalable, objective method for assessing depression and emotional distress, potentially enabling earlier interventions and more consistent diagnoses in mental health.

Objective quantification of mood states using large language models