A Study on the Performance of Generative Pre-trained Transformer (GPT)
  in Simulating Depressed Individuals on the Standardized Depressive Symptom
  Scale
Sijin Cai Nanfeng Zhang Jiaying Zhu Yanjie Liu Yongjin Zhou
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.