GPT-4 generated psychological reports in psychodynamic perspective: a pilot study on quality, risk of hallucination and client satisfaction

• Published in: Frontiers in psychiatry Vol. 16; p. 1473614
• Main Authors: Kim, Namwoo, Lee, Jiseon, Park, Sung Hyeon, On, Yoonseo, Lee, Jieun, Keum, Musung, Oh, Sanghoon, Song, Yoojin, Lee, Junhee, Won, Geun Hui, Shin, Joon Sung, Lho, Silvia Kyungjin, Hwang, Yoon Jung, Kim, Tae-Suk
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 19.03.2025
• Subjects: artificial intelligence; formulation; gpt; hallucination; large language models; Psychiatry; psychodynamic; hallucination; psychopathology; defence mechanisms; formulation; gpt; large language models; artificial intelligence; psychodynamic
• ISSN: 1664-0640; 1664-0640
• DOI: 10.3389/fpsyt.2025.1473614

Recently, there have been active proposals on how to utilize large language models (LLMs) in the fields of psychiatry and counseling. It would be interesting to develop programs with LLMs that generate psychodynamic assessments to help individuals gain insights about themselves, and to evaluate the features of such services. However, studies on this subject are rare. This pilot study aims to evaluate quality, risk of hallucination (incorrect AI-generated information), and client satisfaction with psychodynamic psychological reports generated by GPT-4. The report comprised five components: psychodynamic formulation, psychopathology, parental influence, defense mechanisms, and client strengths. Participants were recruited from individuals distressed by repetitive interpersonal issues. The study was conducted in three steps: 1) Questions provided to participants, designed to create psychodynamic formulations: 14 questions were generated by GPT for inferring psychodynamic formulations, while 6 fixed questions focused on the participants' relationship with their parents. A total of 20 questions were provided. Using participants' responses to these questions, GPT-4 generated the psychological reports. 2) Seven professors of psychiatry from different university hospitals evaluated the quality and risk of hallucinations in the psychological reports by reading the reports only, without meeting the participants. This quality assessment compared the psychological reports generated by GPT-4 with those inferred by the experts. 3) Participants evaluated their satisfaction with the psychological reports. All assessments were conducted using self-report questionnaires based on a Likert scale developed for this study. A total of 10 participants were recruited, and the average age was 32 years. The median response indicated that quality of all five components of the psychological report was similar to the level inferred by the experts. The risk of hallucination was assessed as ranging from unlikely to minor. According to the median response in the satisfaction evaluation, the participants agreed that the report is clearly understandable, insightful, credible, useful, satisfying, and recommendable. This study suggests the possibility that artificial intelligence could assist users by providing psychodynamic interpretations.