使用思维链提示增强抑郁症诊断
Enhancing Depression Diagnosis with Chain-of-Thought Prompting
AI总结：
针对AI在抑郁症检测中易得出先入为主结论的问题，提出使用思维链（CoT）提示评估PHQ-8分数，显著提高了AI估计分数的准确性，旨在增强AI对人类对话的理解以更准确识别心理障碍症状并推动其在医疗领域的应用。
摘要：
在使用AI检测抑郁症迹象时，AI模型习惯性地得出先入为主的结论。我们提出，使用思维链（CoT）提示来评估Patient Health Questionnaire-8（PHQ-8）分数，将提高AI模型确定分数的准确性。我们的研究结果表明，当模型使用CoT进行推理时，与不使用CoT相比，估计的PHQ-8分数平均更接近每位参与者报告的公认真实分数。我们的目标是深化AI模型对人类对话复杂性的理解，使其能够更有效地评估患者的感受和语气，从而能够更准确地识别心理障碍症状；最终，我们希望增强AI模型的能力，使其能够在医疗领域被广泛获取和使用。
英文摘要：
When using AI to detect signs of depressive disorder, AI models habitually draw preemptive conclusions. We theorize that using chain-of-thought (CoT) prompting to evaluate Patient Health Questionnaire-8 (PHQ-8) scores will improve the accuracy of the scores determined by AI models. In our findings, when the models reasoned with CoT, the estimated PHQ-8 scores were consistently closer on average to the accepted true scores reported by each participant compared to when not using CoT. Our goal is to expand upon AI models' understanding of the intricacies of human conversation, allowing them to more effectively assess a patient's feelings and tone, therefore being able to more accurately discern mental disorder symptoms; ultimately, we hope to augment AI models' abilities, so that they can be widely accessible and used in the medical field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
