XrayGPT：使用医学视觉语言模型的胸片摘要。

XrayGPT aims to stimulate research around automated analysis of chest radiographs based on the given x-ray.
XrayGPT 旨在促进围绕基于给定 X 射线的胸片自动分析的研究。
The LLM (Vicuna) is fine-tuned on medical data (100k real conversations between patients and doctors) and ~30k radiology conversations to acquire domain specific and relevant features.
LLM（Vicuna）在医学数据（患者和医生之间的 100k 真实对话）和约 30k 放射学对话上进行了微调，以获得特定领域和相关的特征。
We generate interactive and clean summaries (~217k) from free-text radiology reports of two datasets (MIMIC-CXR and OpenI). These summaries serve to enhance the performance of LLMs through fine-tuning the linear transformation layer on high-quality data. For more details regarding our high-quality summaries, please check Dataset Creation.
我们从两个数据集（MIMIC-CXR 和 OpenI）的自由文本放射学报告中生成交互式和干净的摘要（~217k）。这些总结有助于通过微调高质量数据的线性转换层来提高 LLM 的性能。有关我们高质量摘要的更多详细信息，请查看数据集创建。
We align frozen medical visual encoder (MedClip) with a fune-tuned LLM (Vicuna), using simple linear transformation.
我们使用简单的线性变换将冷冻医学视觉编码器 (MedClip) 与 fune-tuned LLM (Vicuna) 对齐。