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.

翻译：摘要：背景：抑郁症是一种常见的精神障碍，给社会和经济带来沉重负担。当前诊断依赖自我报告和评估量表，存在可靠性问题。亟需客观方法诊断抑郁症。目标：评估GPT技术在抑郁症诊断中的潜力，考察其模拟抑郁个体的能力，并探究抑郁量表的影响。方法：采用三种抑郁评估工具（HAMD-17、SDS、GDS-15），设计两个实验模拟GPT对正常个体和抑郁个体的应答。将GPT的应答与预期结果进行比较，评估其对抑郁症状的理解以及在不同条件下的表现差异。结果：GPT在抑郁评估中的表现符合抑郁症患者和正常个体的评分标准。基于抑郁严重程度观察到部分表现差异，GPT在敏感性更高的量表上表现更优。结论：GPT在抑郁相关评估中能准确模拟抑郁个体和正常个体，但在模拟不同程度抑郁时出现偏差，限制了对轻中度病例的理解。GPT在敏感性更高的量表上表现更优，提示其具有开发更有效抑郁量表的潜力。GPT在抑郁评估中具备重要应用价值，可为临床医生和患者提供支持。