  The rapid development of multimodal large language models (MLLMs), such as
GPT-4V, has led to significant advancements. However, these models still face
challenges in medical multimodal capabilities due to limitations in the
quantity and quality of medical vision-text data, stemming from data privacy
concerns and high annotation costs. While pioneering approaches utilize
PubMed's large-scale, de-identified medical image-text pairs to address these
limitations, they still fall short due to inherent data noise. To tackle this,
we refined medical image-text pairs from PubMed and employed MLLMs (GPT-4V) in
an 'unblinded' capacity to denoise and reformat the data, resulting in the
creation of the PubMedVision dataset with 1.3 million medical VQA samples. Our
validation demonstrates that: (1) PubMedVision can significantly enhance the
medical multimodal capabilities of current MLLMs, showing significant
improvement in benchmarks including the MMMU Health & Medicine track; (2)
manual checks by medical experts and empirical results validate the superior
data quality of our dataset compared to other data construction methods. Using
PubMedVision, we train a 34B medical MLLM HuatuoGPT-Vision, which shows
superior performance in medical multimodal scenarios among open-source MLLMs.
