MeddeX: Medical Report Analyzer A Retrieval-Augmented LLaMA-3 Based System for Evidence-backed Medical Summarization

MeddeX is a comprehensive artificial intelligence system designed to address critical challenges in clinical documentation and medical report analysis. The system leverages a retrieval-augmented generation (RAG) architecture combined with fine-tuned LLaMA-3 large language models to deliver evidence-backed, accurate, and clinically actionable summaries of complex medical documents. Background: Medical professionals routinely encounter lengthy, unstructured clinical reports spanning multiple pages with varied terminology and formatting. This complexity necessitates significant time investment for document interpretation, increasing cognitive load and introducing risk of critical information omission. Traditional manual document processing remains prone to human error and inconsistency. Methodology: The MeddeX system integrates a sophisticated information retrieval pipeline with transformer-based language models. A fine-tuned LLaMA-3 (8B/70B) backbone, optimized via LoRA adaptation, processes clinical text while maintaining domain-specific accuracy. The RAG component enables contextual retrieval from medical knowledge bases, ensuring summaries are grounded in established clinical evidence and guidelines. Architecture: The implementation encompasses (1) a document ingestion and preprocessing module with format standardization, (2) a semantic search engine leveraging dense embeddings for context retrieval, (3) an inference pipeline with fine-tuned LLM components, and (4) a user interface enabling clinicians to validate and refine generated summaries. Key Contributions: This work addresses critical gaps in clinical decision support by automating medical summarization while maintaining high accuracy standards. The system demonstrates potential to significantly reduce documentation burden, improve information accessibility, and enhance patient care workflow efficiency. Applications: Relevant for healthcare institutions, clinical research facilities, and telemedicine platforms requiring scalable medical document analysis.