# Explainable Biomedical Claim Verification with Large Language Models

arXiv: https://arxiv.org/abs/2502.21014 · HTML: https://mlsanity.com/paper/2502.21014/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2025.

## Built on by (most cited first, 2 of 2)

- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [SciClaims: An End-to-End Generative System for Biomedical Claim Analysis](https://mlsanity.com/paper/2503.18526/) (2025)

## Abstract (arXiv)

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.