A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions

• URL: http://arxiv.org/abs/2502.11095v1
• Date: Sun, 16 Feb 2025 12:18:40 GMT
• Title: A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions
• Authors: Hongbin Na, Yining Hua, Zimu Wang, Tao Shen, Beibei Yu, Lilin Wang, Wei Wang, John Torous, Ling Chen,
• Abstract summary: Large language models (LLMs) offer promising solutions in psychotherapy by enhancing the assessment, diagnosis, and treatment of mental health conditions. This survey provides a comprehensive overview of the current landscape of LLM applications in psychotherapy. We present a novel conceptual taxonomy to organize the psychotherapy process into three core components: assessment, diagnosis, and treatment, and examine the challenges and advancements in each area.
• Score: 13.17064228097947
• License:
• Abstract: Mental health remains a critical global challenge, with increasing demand for accessible, effective interventions. Large language models (LLMs) offer promising solutions in psychotherapy by enhancing the assessment, diagnosis, and treatment of mental health conditions through dynamic, context-aware interactions. This survey provides a comprehensive overview of the current landscape of LLM applications in psychotherapy, highlighting the roles of LLMs in symptom detection, severity estimation, cognitive assessment, and therapeutic interventions. We present a novel conceptual taxonomy to organize the psychotherapy process into three core components: assessment, diagnosis, and treatment, and examine the challenges and advancements in each area. The survey also addresses key research gaps, including linguistic biases, limited disorder coverage, and underrepresented therapeutic models. Finally, we discuss future directions to integrate LLMs into a holistic, end-to-end psychotherapy framework, addressing the evolving nature of mental health conditions and fostering more inclusive, personalized care.

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