# Enhancing Depression Diagnosis with Chain-of-Thought Prompting

arXiv: https://arxiv.org/abs/2408.14053 · HTML: https://mlsanity.com/paper/2408.14053/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)

## Built on by (most cited first, 2 of 2)

- [Reliability of Large Language Model Generated Clinical Reasoning in Assisted Reproductive Technology: Blinded Comparative Evaluation Study](https://mlsanity.com/paper/2510.16095/) (2025)
- [Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasets](https://mlsanity.com/paper/2607.03540/) (2026)

## Abstract (arXiv)

When using AI to detect signs of depressive disorder, AI models habitually draw preemptive conclusions. We theorize that using chain-of-thought (CoT) prompting to evaluate Patient Health Questionnaire-8 (PHQ-8) scores will improve the accuracy of the scores determined by AI models. In our findings, when the models reasoned with CoT, the estimated PHQ-8 scores were consistently closer on average to the accepted true scores reported by each participant compared to when not using CoT. Our goal is to expand upon AI models' understanding of the intricacies of human conversation, allowing them to more effectively assess a patient's feelings and tone, therefore being able to more accurately discern mental disorder symptoms; ultimately, we hope to augment AI models' abilities, so that they can be widely accessible and used in the medical field.