Ohse Julia, Hadžić Bakir, Mohammed Parvez, Peperkorn Nicolina, Fox Janosch, Krutzki Joshua, Lyko Alexander, Mingyu Fan, Zheng Xiaohu, Rätsch Matthias, Shiban Youssef
Clinical Psychology Department, PFH University of Applied Sciences, Göttingen, Germany.
ViSiR, Reutlingen University, Reutlingen, Germany.
Sci Rep. 2024 Dec 16;14(1):30498. doi: 10.1038/s41598-024-82192-2.
While the potential of Artificial Intelligence (AI)-particularly Natural Language Processing (NLP) models-for detecting symptoms of depression from text has been vastly researched, only a few studies examine such potential for the detection of social anxiety symptoms. We investigated the ability of the large language model (LLM) GPT-4 to correctly infer social anxiety symptom strength from transcripts obtained from semi-structured interviews. N = 51 adult participants were recruited from a convenience sample of the German population. Participants filled in a self-report questionnaire on social anxiety symptoms (SPIN) prior to being interviewed on a secure online teleconference platform. Transcripts from these interviews were then evaluated by GPT-4. GPT-4 predictions were highly correlated (r = 0.79) with scores obtained on the social anxiety self-report measure. Following the cut-off conventions for this population, an F accuracy score of 0.84 could be obtained. Future research should examine whether these findings hold true in larger and more diverse datasets.
虽然人工智能（AI）——特别是自然语言处理（NLP）模型——从文本中检测抑郁症症状的潜力已得到广泛研究，但只有少数研究考察了其检测社交焦虑症状的潜力。我们调查了大语言模型（LLM）GPT-4从半结构化访谈记录中正确推断社交焦虑症状强度的能力。从德国人群的便利样本中招募了N = 51名成年参与者。参与者在安全的在线电话会议平台上接受访谈之前，填写了一份关于社交焦虑症状的自我报告问卷（SPIN）。然后，GPT-4对这些访谈的记录进行评估。GPT-4的预测与社交焦虑自我报告测量中获得的分数高度相关（r = 0.79）。按照该人群的截断标准，可以获得0.84的F准确率得分。未来的研究应考察这些发现在更大、更多样化的数据集中是否成立。
