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.

评估GPT技术在诊断抑郁症方面的潜力，研究其模拟抑郁症状和抑郁量表影响能力，发现GPT可以准确模拟抑郁症状和正常个体的抑郁相关评估，但在模拟不同程度的抑郁症状方面存在误差，并且对敏感程度高的量表表现更好，具有重要的抑郁症评估潜力。

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

生成式预训练转换器（GPT）在模拟标准抑郁症状量表上抑郁个体表现的研究