Application progress on large language models in the field of psychological care

Overview

This review examines the application of large language models (LLMs) in psychological care contexts, focusing on three primary domains: assessment, intervention, and health education. The work addresses the integration of artificial intelligence technologies within mental health service delivery systems, particularly in the context of China's mental health infrastructure. The authors identify both the current state of LLM deployment in psychological care settings and the associated risks and challenges that emerge during implementation. The review is positioned to guide researchers and practitioners in leveraging LLM capabilities while addressing operational and ethical concerns inherent to their use in clinical and therapeutic contexts.

Methods and approach

The study employs a systematic review methodology to survey existing applications of large language models across multiple domains of psychological care. The review synthesizes literature and practice examples related to LLM implementation in assessment protocols, intervention strategies, and health education initiatives. The authors adopt a risk-assessment framework to identify challenges and vulnerabilities associated with LLM deployment in mental health settings. Based on this analysis, they develop targeted strategies intended to mitigate identified risks. The approach is structured to provide actionable guidance for integrating LLM technologies into established mental health service frameworks, with particular attention to the Chinese mental health system's organizational and operational requirements.

Key Findings

The review documents the current state of LLM applications across assessment, intervention, and health education within psychological care. Specific risks and challenges associated with LLM implementation in mental health contexts are catalogued, though the extract does not detail individual findings. The authors present targeted strategies designed to address the identified challenges and optimize LLM integration. These strategies are oriented toward improving workflow efficiency within mental health service systems. The findings are intended to support the deep integration of LLM technologies with psychological care practices, suggesting that implementation barriers have been sufficiently characterized to enable strategic planning and risk mitigation in clinical settings.

Implications

The review provides a framework for researchers and practitioners to navigate LLM integration in psychological care settings, with direct relevance to mental health service system optimization. The proposed strategies address operational efficiency improvements and risk management in LLM deployment. The work contributes to the development of evidence-based protocols for artificial intelligence utilization in clinical mental health contexts. The focus on China's mental health service system suggests implications for health policy and service delivery reform in resource allocation and technological infrastructure development. The review positions LLM technologies as tools for enhancing service capacity and workflow optimization, contingent on systematic risk management and strategic implementation planning.