RAGnosis: Retrieval-Augmented Generation for Enhanced Medical Decision Making

Abstract

Abstract We present RAGnosis, a fully offline, retrieval-augmented framework for interpreting unstructured clinical text using open-weight large language models. As a proof of concept, we apply RAGnosis to the task of paravalvular leak (PVL) classification from cardiac catheterization reports, a process that typically requires slow, expert-driven interpretation. The system combines local OCR, semantic retrieval, and instruction-tuned LLMs to generate evidence-backed predictions and explanations grounded in real clinical documentation. We evaluate four models (DeepSeek 70B, Gemma 27B, Mistral 7B, LLaMA 3B) across 100 reports, analyzing classification accuracy, explanation quality, and retrieval relevance. Results highlight tradeoffs between fluency and reliability, with DeepSeek demonstrating the most consistent performance. By operating entirely on-prem and supporting modular integration, RAGnosis provides a scalable and interpretable foundation for clinical NLP that delivers not just answers but traceable reasoning.

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