This research introduces a Chain-of-Thought prompting approach that improves how large language models detect depression from text, creating a step-by-step reasoning process that enhances diagnostic accuracy.

• Transforms emotional analysis into structured reasoning for depression detection
• Improves transparency in AI mental health assessments through explicit reasoning steps
• Demonstrates significant accuracy improvements over standard prompting techniques
• Provides a framework for more reliable mental health screening tools

This innovation addresses critical challenges in mental healthcare by enabling more accurate, explainable depression detection systems that could help identify at-risk individuals earlier and with greater reliability.

Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models