  The increasing interest in developing Artificial Intelligence applications in
the medical domain, suffers from the lack of high-quality dataset, mainly due
to privacy-related issues. Moreover, the recent rising of Multimodal Large
Language Models (MLLM) leads to a need for multimodal medical datasets, where
clinical reports and findings are attached to the corresponding CT or MR scans.
This paper illustrates the entire workflow for building the data set MedPix
2.0. Starting from the well-known multimodal dataset
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 where noisy samples were removed, thus creating a MongoDB
database. Along with the dataset, 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 MLLMs. To enforce this point, we also
propose a CLIP-based model trained on MedPix 2.0 for scan classification tasks.
