MedInsight Pro: An Explainable Hybrid NLP and LLM Fusion Framework for Radiology Report Interpretation

Radiology reports are often unstructured, containing ambiguous phrases and negations that make automated interpretation difficult. This paper introducesMedInsight Pro, a hybrid clinical NLP (Natural Language Processing) framework that fuses deterministic parsing with LLM (Large Language Model) augmentation to extract meaningful insights from chest X-ray impressions. The system combines rule-based negation detection, dependency parsing, and lexicon-driven keyword matching with contextual expansion powered by Groq’s LLM. On an expanded CheXpert-derived cohort of 2 020 reports (1 020 real and 1 000 augmented samples), MedInsight Pro achieved an average macro F1-score of 89.7%, with overall precision of 91.8%, recall of 89.4%, and negation detection accuracy of 95.1%. These results surpass keyword-only, LLM-only, and CheXpert labeler baselines. Deployed via a Streamlit interface, the system also offers explainability through clause heatmaps, radar charts, robustness visualizations under synthetic domain shift, and a clinical Q&A interface. The findings highlight the practicality of hybrid NLP–LLM pipelines as scalable, transparent decision-support tools for radiologists in real-world clinical settings. The system demonstrates strong performance in structured label extraction and offers interpretability through visualization modules and an LLM-assisted reasoning laye.