Combating Biomedical Misinformation through Multi-modal Claim Detection and Evidence-based Verification

2025-09-17

AI Summary

Biomedical misinformation—such as vaccine hesitancy and pseudoscientific therapies—undermines public health trust. To address this, we propose CER, a framework for automated detection and interpretable verification of biomedical claims. CER integrates scientific literature retrieval, large language model (LLM)-based evidence-augmented reasoning, and supervised factual consistency prediction. Crucially, it constrains LLM generation using high-quality, retrieved scientific evidence, substantially mitigating hallucination and enabling robust cross-dataset generalization. Evaluated on three authoritative benchmarks—HealthFC, BioASQ-7b, and SciFact—CER achieves state-of-the-art performance. All code, datasets, and models are publicly released to foster reproducible, transparent medical fact-checking research.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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

Problem

• Detecting biomedical misinformation through multimodal claim analysis
• Verifying health claims using evidence-based scientific sources
• Overcoming domain-specific challenges in automated fact-checking

Innovation

• Multi-modal claim detection framework
• Evidence retrieval with large language models
• Supervised veracity prediction integration