MeddeX: A Retrieval-Augmented LLaMA-3 System with Gemini Vision for Evidence-Backed Medical Report Analysis
Read the original on arXiv Computation and Language →MeddeX is a medical report analyzer that fuses a QLoRA‑fine‑tuned LLaMA‑3 (8B) model with Retrieval‑Augmented Generation and a Gemini Vision layer to produce concise, evidence‑backed summaries of medical documents. It processes text‑native PDFs, scanned PDFs, and radiological images via a hybrid extraction pipeline featuring a garbage‑text detection mechanism for corrupted or UTF‑16‑encoded files. The system adds report‑scoped retrieval, an automated longitudinal comparison engine, and a self‑healing backfill pipeline, and is backed by a FastAPI backend, Supabase vector store, and Next.js frontend. Evaluation shows significant gains over the base LLaMA‑3‑Med42‑8B model across ROUGE, BLEU, and BERTScore metrics, underscoring the value of domain‑specific fine‑tuning.
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