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Large language models (LLMs) offer potential for psychotherapy, but their roles require systematic understanding. This perspective proposes a taxonomy of LLM roles, addressing key computational and ethical challenges in mental healthcare.

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Area of Science:

  • Psychiatry and Mental Health
  • Artificial Intelligence in Healthcare
  • Computational Psychology

Background:

  • Large language models (LLMs) show promise for improving psychotherapy accessibility, personalization, and engagement.
  • A comprehensive understanding of specific LLM roles in mental healthcare is currently lacking.

Purpose of the Study:

  • To propose a novel taxonomy categorizing the roles of LLMs in psychotherapy.
  • To delineate six distinct LLM roles based on AI autonomy and emotional engagement.
  • To identify and discuss critical computational and ethical challenges associated with LLM integration in mental health.

Main Methods:

  • Conceptual analysis and synthesis of current literature on LLMs and psychotherapy.
  • Development of a taxonomy based on two key dimensions: artificial intelligence autonomy and emotional engagement.
  • Identification of associated computational and ethical considerations.

Main Results:

  • A proposed taxonomy outlines six specific roles for LLMs in psychotherapy.
  • Key challenges identified include emotion recognition, memory retention, privacy, and emotional dependency.
  • Recommendations for addressing these challenges are provided.

Conclusions:

  • LLMs present significant opportunities for augmenting psychotherapy.
  • Systematic frameworks and careful consideration of ethical and computational challenges are crucial for responsible LLM implementation in mental healthcare.
  • Further research is needed to navigate the complexities of AI in therapeutic settings.