标题： GPT-4在临床抑郁症评估中的应用：一项基于大模型的试点研究 显示英文标题

标题： GPT-4 on Clinic Depression Assessment: An LLM-Based Pilot Study

摘要： 抑郁症已影响了全球数百万人，并已成为最普遍的心理障碍之一。 早期心理障碍检测可以为公共卫生机构节省成本，并避免其他主要共病的发生。 此外，专业人员的短缺是一个关键问题，因为临床抑郁症诊断高度依赖专家并耗时。 在本研究中，我们探讨基于转录分析使用GPT-4进行临床抑郁症评估的可行性。 我们检查模型将患者访谈分类为二元类别（抑郁和非抑郁）的能力。 进行比较分析时考虑提示复杂性（例如，使用简单和复杂的提示）以及不同的温度设置，以评估提示复杂性和随机性对模型性能的影响。 结果表明，GPT-4在不同配置下的准确性和F1分数表现出较大的变化，对于复杂提示，在较低温度值（0.0-0.2）时表现出最佳性能。 然而，超过一定阈值（温度>=0.3）后，随机性和性能之间的关系变得不可预测，降低了提示复杂性的收益。 这些发现表明，尽管GPT-4在临床评估方面显示出潜力，但提示和模型参数的配置需要仔细校准以确保结果的一致性。 这项初步研究有助于理解提示工程与大型语言模型之间的动态关系，为未来在临床环境中开发人工智能工具提供了见解。 显示英文摘要

摘要： Depression has impacted millions of people worldwide and has become one of the most prevalent mental disorders. Early mental disorder detection can lead to cost savings for public health agencies and avoid the onset of other major comorbidities. Additionally, the shortage of specialized personnel is a critical issue because clinical depression diagnosis is highly dependent on expert professionals and is time consuming. In this study, we explore the use of GPT-4 for clinical depression assessment based on transcript analysis. We examine the model's ability to classify patient interviews into binary categories: depressed and not depressed. A comparative analysis is conducted considering prompt complexity (e.g., using both simple and complex prompts) as well as varied temperature settings to assess the impact of prompt complexity and randomness on the model's performance. Results indicate that GPT-4 exhibits considerable variability in accuracy and F1-Score across configurations, with optimal performance observed at lower temperature values (0.0-0.2) for complex prompts. However, beyond a certain threshold (temperature >= 0.3), the relationship between randomness and performance becomes unpredictable, diminishing the gains from prompt complexity. These findings suggest that, while GPT-4 shows promise for clinical assessment, the configuration of the prompts and model parameters requires careful calibration to ensure consistent results. This preliminary study contributes to understanding the dynamics between prompt engineering and large language models, offering insights for future development of AI-powered tools in clinical settings.