A Multimodal Biomedical Foundation Model Trained from Fifteen Million Image-Text Pairs

Biomedical data are inherently multimodal, comprising physical measurements and natural-language narratives. A generalist biomedical artificial intelligence (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 Radiological Society of North America (RSNA) pneumonia detection.

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 aka.ms/biomedclip to facilitate future research in multimodal biomedical AI.