Big data in ophthalmology: comparative databases and research applications

This review aims to highlight the expanding role of big data in ophthalmology, provide a comparison of the most prominent databases, and their use in glaucoma-specific research. Understanding the strengths and limitations of each database allows researchers to tailor their research questions appropriately.

Several large-scale databases have emerged in ophthalmology research. Some databases offer detailed ocular exam findings and imaging, supporting artificial intelligence-driven diagnostics and treatment evaluation. Others are broader in scope, providing real-world population data to study trends and outcomes. Some databases even integrate genomic and systemic data, enabling novel explorations of disease risk and health disparities. Collectively, big data has enabled large-scale studies on a wide range of topics, advancing the field of ophthalmology in every aspect.

Big data platforms are transforming ophthalmology research, from uncovering systemic-ocular relationships to enabling artificial intelligence applications. Researchers can select platforms based on the availability of imaging, systemic data, or genomic information to better address specific research questions. Doing so can enhance precision medicine, address care disparities, and drive innovation in disease pathophysiology discovery, detection, and management.