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The emergence and rapid development of large language models (llms) have shown the potential to address these mental health demands This review aims to assess the current role of llms within psychiatry research, aiming to identify their strengths, limitations, and potential future applications. However, a comprehensive review summarizing the application areas, processes, and performance comparisons of llms in mental health has been lacking until now.
By systematically collating and assessing the evidence from current studies, our work analyzes models, methodologies, data sources, and outcomes, thereby highlighting the potential of llms in mental health, the challenges they present, and the prospects for their clinical use. As llm technology advances rapidly, it holds the potential to alter traditional mental health care paradigms The data extraction process was jointly conducted by two reviewers (zg, kl), focusing on examining the application scenarios, model architecture, data sources, methodologies used, and main outcomes from selected studies on llms in mental health.
This systematic review aims to critically assess the use of llms in mental health, specifically focusing on their applicability and efficacy in early screening, digital interventions, and clinical settings.
Considering these promises and challenges, a scoping review of the current applications of llms in mental health care is essential from the perspective of psychiatrists and clinical. Out of an initial 313 articles, 34 were selected based on their relevance to llms applications in mental health care and the rigor of their reported outcomes The review identified various llms applications in mental health care, including diagnostics, therapy, and enhancing patient engagement.
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