High-throughput biomedical relation extraction for semi-structured web articles empowered by large language models

Our study demonstrates that LLMs can serve as effective engines for high-throughput biomedical relation extraction, with domain-adapted and parameter-efficient models offering practical advantages. The framework is scalable and broadly adaptable, enabling efficient extraction of diverse biomedical relations across heterogeneous semi-structured websites. Beyond technical performance, the ability to extract reliable biomedical relations at scale can directly benefit clinical applications, such as enriching biomedical knowledge graphs, supporting evidence-based guideline development, and ultimately assisting clinicians in accessing structured medical knowledge for decision-making.