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

Year: 2024

Type: article

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 F 1 accuracy score of 0.84 could be obtained. Future research should examine whether these findings hold true in larger and more diverse datasets.

Source: Scientific Reports

DOI: 10.1038/s41598-024-82192-2