Explainable Biomedical Claim Verification with Large Language Models

TL;DR

This paper introduces an interactive system that uses large language models and explainability techniques to verify biomedical claims by analyzing scientific studies, enhancing transparency and user trust in AI-assisted healthcare decisions.

Contribution

It presents a novel interactive framework combining LLMs, model explanations, and user input for biomedical claim verification, emphasizing transparency and interpretability.

Findings

Effective integration of LLMs with explanation tools like SHAP.

Improved transparency in biomedical claim verification.

Potential to support human-AI collaboration in healthcare.

Abstract

Verification of biomedical claims is critical for healthcare decision-making, public health policy and scientific research. We present an interactive biomedical claim verification system by integrating LLMs, transparent model explanations, and user-guided justification. In the system, users first retrieve relevant scientific studies from a persistent medical literature corpus and explore how different LLMs perform natural language inference (NLI) within task-adaptive reasoning framework to classify each study as "Support," "Contradict," or "Not Enough Information" regarding the claim. Users can examine the model's reasoning process with additional insights provided by SHAP values that highlight word-level contributions to the final result. This combination enables a more transparent and interpretable evaluation of the model's decision-making process. A summary stage allows users to consolidate the results by selecting a result with narrative justification generated by LLMs. As a result, a consensus-based final decision is summarized for each retrieved study, aiming safe and accountable AI-assisted decision-making in biomedical contexts. We aim to integrate this explainable verification system as a component within a broader evidence synthesis framework to support human-AI collaboration.