  Biomedical data is inherently multimodal, comprising physical measurements
and natural language narratives. A generalist biomedical AI model needs to
simultaneously process different modalities of data, including text and images.
Therefore, training an effective generalist biomedical model requires
high-quality multimodal data, such as parallel image-text pairs. Here, we
present PMC-15M, a novel dataset that is two orders of magnitude larger than
existing biomedical multimodal datasets such as MIMIC-CXR, and spans a diverse
range of biomedical image types. PMC-15M contains 15 million biomedical
image-text pairs collected from 4.4 million scientific articles. Based on
PMC-15M, we have pretrained BiomedCLIP, a multimodal foundation model, with
domain-specific adaptations tailored to biomedical vision-language processing.
We conducted extensive experiments and ablation studies on standard biomedical
imaging tasks from retrieval to classification to visual question-answering
(VQA). BiomedCLIP achieved new state-of-the-art results in a wide range of
standard datasets, substantially outperforming prior approaches. Intriguingly,
by large-scale pretraining on diverse biomedical image types, BiomedCLIP even
outperforms state-of-the-art radiology-specific models such as BioViL in
radiology-specific tasks such as RSNA pneumonia detection. In summary,
BiomedCLIP is a fully open-access foundation model that achieves
state-of-the-art performance on various biomedical tasks, paving the way for
transformative multimodal biomedical discovery and applications. We release our
models at https://aka.ms/biomedclip to facilitate future research in multimodal
biomedical AI.
