Combining Anti-Hallucination Strategies for Reliable LLM-Based Clinical Information Extraction

We present a novel LLM-based approach for medical concept extraction that combines multiple anti-hallucination strategies. Our Streamlit web application enables flexible pipeline configuration through ensemble methods, Chain-of-Verification, contextual grounding, and LLM-as-a-Judge mechanisms. This modular framework addresses the critical challenge of hallucinations in clinical text processing, particularly for smaller models required in privacy-constrained hospital environments.