GPT-4 shows potential for identifying social anxiety from clinical interview data

Sci Rep. 2024 Dec 16;14(1):30498. doi: 10.1038/s41598-024-82192-2.

Abstract

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 F1 accuracy score of 0.84 could be obtained. Future research should examine whether these findings hold true in larger and more diverse datasets.

Keywords: Anxiety; Artificial intelligence; GPT-4; Generative pre-trained transformers; Natural language processing; Social anxiety.

MeSH terms

  • Adult
  • Aged
  • Anxiety* / diagnosis
  • Artificial Intelligence
  • Female
  • Humans
  • Male
  • Middle Aged
  • Natural Language Processing
  • Phobia, Social / diagnosis
  • Self Report
  • Surveys and Questionnaires
  • Young Adult