RAGnosis: Retrieval-Augmented Generation for Enhanced Medical Decision Making

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. Competing Interest Statement The authors have declared no competing interest. Funding Statement American Heart Association Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB 2010P000292; Department of Surgery, Brigham and Women's Hospital gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Due to data privacy, the data used for this research will not be available online.