The increasing interest in developing Artificial Intelligence applications in the medical domain, suffers from the lack of high-quality data set, mainly due to privacy-related issues. In addition, the recent increase in large multimodal models (LMM) leads to the need for multimodal medical data sets, where clinical reports and findings are attached to the corresponding CT or MRI scans. This paper illustrates the entire workflow for building the MedPix 2.0 data set. Starting with the well-known multimodal data set MedPix\textsuperscript{\textregistered}, mainly used by physicians, nurses, and healthcare students for Continuing Medical Education purposes, a semi-automatic pipeline was developed to extract visual and textual data followed by a manual curing procedure in which noisy samples were removed, thus creating a MongoDB database. Along with the data set, we developed a GUI aimed at navigating efficiently the MongoDB instance and obtaining the raw data that can be easily used for training and/or fine-tuning LMMs. To enforce this point, in this work, we first recall DR-Minerva, a RAG-based LMM trained using MedPix 2.0. DR-Minerva predicts the body part and the modality used to scan its input image. We also propose the extension of DR-Minerva with a Knowledge Graph that uses Llama 3.1 Instruct 8B, and leverages MedPix 2.0. The resulting architecture can be queried in a end-to-end manner, as a medical decision support system. MedPix 2.0 is available on GitHub. \url{https://github.com/CHILab1/MedPix-2.0}

翻译：在医学领域开发人工智能应用日益受到关注，但主要由于隐私相关问题，高质量数据集的缺乏构成了显著障碍。此外，近期大型多模态模型的兴起，催生了对多模态医学数据集的需求，这类数据集需将临床报告和发现与对应的CT或MRI扫描图像关联起来。本文阐述了构建MedPix 2.0数据集的完整工作流程。以主要用于医师、护士和医学生进行继续医学教育的知名多模态数据集MedPix®为起点，我们开发了一个半自动处理流程来提取视觉和文本数据，随后通过人工筛选程序移除噪声样本，从而创建了一个MongoDB数据库。伴随该数据集，我们还开发了一个图形用户界面，旨在高效导航MongoDB实例并获取可直接用于训练和/或微调大型多模态模型的原始数据。为强化此点，本文首先回顾了基于检索增强生成的大型多模态模型DR-Minerva，该模型使用MedPix 2.0进行训练，能够预测输入图像的身体部位和扫描模态。我们还提出了DR-Minerva的扩展方案，通过集成知识图谱并利用Llama 3.1 Instruct 8B模型，构建了一个可端到端查询的医疗决策支持系统架构。MedPix 2.0已在GitHub上开源。\url{https://github.com/CHILab1/MedPix-2.0}