Combating Biomedical Misinformation through Multi-modal Claim Detection and Evidence-based Verification
This addresses misinformation in healthcare, such as vaccine hesitancy, by improving automated fact-checking for biomedical claims, though it is incremental as it builds on existing machine learning and NLP methods.
The paper tackles the challenge of validating biomedical claims by introducing CER, a framework that integrates evidence retrieval, reasoning via large language models, and supervised veracity prediction, achieving state-of-the-art performance on expert-annotated datasets like HealthFC, BioASQ-7b, and SciFact.
Misinformation in healthcare, from vaccine hesitancy to unproven treatments, poses risks to public health and trust in medical systems. While machine learning and natural language processing have advanced automated fact-checking, validating biomedical claims remains uniquely challenging due to complex terminology, the need for domain expertise, and the critical importance of grounding in scientific evidence. We introduce CER (Combining 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 models with advanced retrieval techniques for high-quality biomedical scientific evidence, CER effectively mitigates the risk of hallucinations, ensuring that generated outputs are grounded in verifiable, 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