A Large-Scale Vision-Language Dataset Derived from Open Scientific Literature to Advance Biomedical Generalist AI

Despite the excitement behind biomedical artificial intelligence (AI), access to high-quality, diverse, and large-scale data - the foundation for modern AI systems - is still a bottleneck to unlocking its full potential. T o address this gap, we introduce BIOMEDICA an open-source dataset derived from the PubMed Central Open Access subset, containing over 6 million scientific articles and 24 million image-text pairs, along with 27 metadata fields, including expert human annotations. T o overcome the challenges of accessing our large-scale dataset, we offer a web platform with tools that enable both targeted content retrieval and on-demand data access without downloading the entire dataset, facilitating seamless integration with AI systems. W e demonstrate the utility of the BIOMEDICA dataset by building embedding models, chat-style models, and retrieval-augmented chat agents. Notably, all our AI models surpass previous open systems in their respective categories, underscoring the critical role of diverse, high-quality, and large-scale biomedical data.