Enhancing Depression Diagnosis with Chain-of-Thought Prompting
This work addresses improving AI-assisted depression diagnosis for medical applications, but it appears incremental as it applies an existing prompting technique to a specific domain.
The paper tackled the problem of AI models making premature conclusions in depression diagnosis by using chain-of-thought prompting to evaluate PHQ-8 scores, resulting in estimated scores that were consistently closer to true scores on average.
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.