用证据和推理结合的方式，精准验证医学真假信息。
Combining Evidence and Reasoning for Biomedical Fact-Checking
- 融合科学文献检索与大模型推理，确保结论有据可查。
- 在多个医学数据集上达到顶尖准确率，跨数据集泛化能力强。
- 适合医疗研究者、AI健康应用开发者使用。
医疗领域中的错误信息，如疫苗犹豫和未经证实的治疗方法，对公众健康和医疗体系信任构成威胁。尽管机器学习与自然语言处理推动了自动化事实核查的发展，但生物医学声明的验证仍因术语复杂、需专业知识且必须基于科学证据而极具挑战。本文提出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。通过将大模型文本生成能力与高质量生物医学文献检索技术结合，有效降低幻觉风险，确保输出结果可追溯、可验证。在专家标注数据集（HealthFC、BioASQ-7b、SciFact）上的评估显示，CER表现达到当前最优水平，并展现出良好的跨数据集泛化能力。代码与数据已公开：https://github.com/PRAISELab-PicusLab/CER，以保障透明性与可复现性。
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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