High-fidelity data curation from scientific literature can create large-scale medical multimodal datasets that rival or exceed models trained on much larger datasets, enabling better medical AI without requiring new data collection.
MedPMC is a framework that automatically extracts and curates 11 million high-quality medical image-text pairs from 6.1 million PubMed Central articles. The resulting dataset trains multimodal models that significantly outperform existing biomedical baselines on medical imaging tasks, with 95.3% of extracted images validated as medically relevant by human reviewers.