A Study on the Performance of Generative Pre-trained Transformer (GPT) in Simulating Depressed Individuals on the Standardized Depressive Symptom Scale

TL;DR

This study evaluates GPT's ability to simulate and assess depression using standard scales, revealing its potential to aid clinicians despite limitations in simulating mild to moderate depression cases.

Contribution

It demonstrates GPT's capacity to accurately simulate depression assessments and highlights its potential for improving depression diagnosis tools.

Findings

GPT aligns with scoring criteria for depression and normal individuals.

Performance varies with depression severity and scale sensitivity.

GPT shows potential for developing more effective depression assessment scales.

Abstract

Background: Depression is a common mental disorder with societal and economic burden. Current diagnosis relies on self-reports and assessment scales, which have reliability issues. Objective approaches are needed for diagnosing depression. Objective: Evaluate the potential of GPT technology in diagnosing depression. Assess its ability to simulate individuals with depression and investigate the influence of depression scales. Methods: Three depression-related assessment tools (HAMD-17, SDS, GDS-15) were used. Two experiments simulated GPT responses to normal individuals and individuals with depression. Compare GPT's responses with expected results, assess its understanding of depressive symptoms, and performance differences under different conditions. Results: GPT's performance in depression assessment was evaluated. It aligned with scoring criteria for both individuals with depression and normal individuals. Some performance differences were observed based on depression severity. GPT performed better on scales with higher sensitivity. Conclusion: GPT accurately simulates individuals with depression and normal individuals during depression-related assessments. Deviations occur when simulating different degrees of depression, limiting understanding of mild and moderate cases. GPT performs better on scales with higher sensitivity, indicating potential for developing more effective depression scales. GPT has important potential in depression assessment, supporting clinicians and patients.