用多模态证据验证医疗假信息，防止AI胡说八道。
Combating Biomedical Misinformation through Multi-modal Claim Detection and Evidence-based Verification
- 结合科学文献检索与大模型推理，自动验证医疗声明真伪。
- 在HealthFC等数据集上达到当前最好效果，跨数据集泛化强。
- 适合医学信息审核、AI辅助诊疗系统开发者使用。
医疗领域中的虚假信息，如疫苗犹豫和未经证实的疗法，威胁公共健康并削弱对医疗系统的信任。尽管机器学习和自然语言处理已推动自动化事实核查发展，但生物医学声明的验证仍具挑战性，原因包括专业术语复杂、需领域知识，且必须基于科学证据。我们提出CER（Combining Evidence and Reasoning）框架，整合科学证据检索、大语言模型推理与监督式可信度预测。通过将大模型的文本生成能力与高质量生物医学证据的先进检索技术结合，CER有效降低幻觉风险，确保输出可验证、基于证据。在专家标注数据集（HealthFC、BioASQ-7b、SciFact）上的评估显示其达到当前最优性能，并展现出良好的跨数据集泛化能力。代码与数据已公开以保障透明性和可复现性：https://github.com/PRAISELab-PicusLab/CER
原文摘要 · Abstract (English)
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
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