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

2025-02-16

AI Summary

This study addresses critical limitations of large language models (LLMs) in psychotherapy—including linguistic bias, narrow diagnostic coverage, and insufficient representation of therapeutic modalities—by proposing the first three-dimensional application taxonomy spanning assessment, diagnosis, and intervention. Employing a mixed-method systematic review integrating bibliometric analysis and qualitative thematic coding, the work bridges clinical psychology frameworks with AI capability mapping to uncover cross-therapeutic adaptability disparities and cross-cultural linguistic biases. It rigorously delineates LLM performance boundaries across core clinical tasks: symptom identification, severity estimation, and cognitive assessment. Crucially, it establishes the first structural alignment between major evidence-based therapies—such as cognitive behavioral therapy (CBT), acceptance and commitment therapy (ACT), and psychodynamic approaches—and LLM technical capabilities. The findings provide both a theoretical foundation and an implementation roadmap for developing end-to-end, personalized, and culturally inclusive digital psychotherapy systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Applications

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Enhancing mental health interventions with LLMs
• Organizing psychotherapy into assessment, diagnosis, treatment
• Addressing linguistic biases and improving disorder coverage

Innovation

• LLMs enhance psychotherapy interactions
• Novel taxonomy organizes therapy process
• Future integrates LLMs for personalized care