Combining Evidence and Reasoning for Biomedical Fact-Checking

Misinformation in healthcare, from vaccine hesitancy to unproven treatments, poses risks to public health and trust in medical sys- tems. While machine learning and natural language processing have advanced automated fact-checking, validating biomedical claims remains uniquely challenging due to complex terminol- ogy, the need for domain expertise, and the critical importance of grounding in scientific evidence. We introduce CER (Combin- ing Evidence and Reasoning), a novel framework for biomedical fact-checking that integrates scientific evidence retrieval, reasoning via large language models, and supervised veracity prediction. By integrating the text-generation capabilities of large language mod- els with advanced retrieval techniques for high-quality biomedical scientific evidence, CER effectively mitigates the risk of halluci- nations, ensuring that generated outputs are grounded in veri- fiable, evidence-based sources. Evaluations on expert-annotated datasets (HealthFC, BioASQ-7b, SciFact) demonstrate state-of-the- art performance and promising cross-dataset generalization. Code and data are released for transparency and reproducibility: https: //github.com/PRAISELab-PicusLab/CER.