Abstract：
The ability of Large Language Models (LLMs) to analyze and respond to freely written text is causing increasing excitement in the field of psychiatry; the application of such models presents unique opportunities and challenges for psychiatric applications. This review article seeks to offer a comprehensive overview of LLMs in psychiatry, their model architecture, potential use cases, and clinical considerations. LLM frameworks such as ChatGPT/GPT-4 are trained on huge amounts of text data that are sometimes fine-tuned for specific tasks. This opens up a wide range of possible psychiatric applications, such as accurately predicting individual patient risk factors for specific disorders, engaging in therapeutic intervention, and analyzing therapeutic material, to name a few. However, adoption in the psychiatric setting presents many challenges, including inherent limitations and biases in LLMs, concerns about explainability and privacy, and the potential damage resulting from produced misinformation. This review covers potential opportunities and limitations and highlights potential considerations when these models are applied in a real-world psychiatric context.
摘要：
大型语言模型（LLM）分析和响应自由书写文本的能力在精神病学领域引起了越来越多的兴奋；此类模型的应用为精神病学应用带来了独特的机遇和挑战。这篇综述文章旨在全面概述精神病学中的LLM，他们的模型架构，潜在的用例，和临床考虑。诸如ChatGPT/GPT-4之类的LLM框架是针对大量文本数据进行训练的，这些文本数据有时会针对特定任务进行微调。这开辟了广泛的可能的精神病学应用，例如准确预测特定疾病的个体患者风险因素，从事治疗干预，分析治疗材料，仅举几例。然而,在精神病学环境中收养会带来许多挑战，包括LLM的固有限制和偏见，对可解释性和隐私的担忧，以及产生的错误信息造成的潜在损害。这篇综述涵盖了潜在的机会和局限性，并强调了在现实世界的精神病学背景下应用这些模型时的潜在考虑因素。
