Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Psychiatry Res. 2024 Sep;339:116026. doi: 10.1016/j.psychres.2024.116026. Epub 2024 Jun 11.
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.
大型语言模型（LLMs）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会和挑战。本文旨在全面概述精神病学中的 LLM，包括它们的模型架构、潜在的用例和临床注意事项。ChatGPT/GPT-4 等 LLM 框架是在大量文本数据上进行训练的，有时会针对特定任务进行微调。这为许多可能的精神病学应用打开了大门，例如准确预测特定障碍患者的个体风险因素、参与治疗干预以及分析治疗材料等。然而，在精神病学环境中的采用存在许多挑战，包括 LLM 固有的局限性和偏见、对可解释性和隐私的担忧，以及产生错误信息可能造成的潜在危害。本文综述了潜在的机会和局限性，并强调了在将这些模型应用于现实世界的精神病学环境时需要考虑的问题。
