TrustMed-RAG: A Verification-Driven Retrieval-Augmented Generation Framework for Trustworthy Medical Decision Support

This repository contains the official implementation of TrustMed-RAG, an advanced Retrieval-Augmented Generation (RAG) framework tailored specifically for high-stakes clinical decision support. Unlike standard RAG pipelines that blindly trust retrieved snippets, TrustMed-RAG introduces a rigorous, multi-stage Verification-Driven Architecture. It mathematically cross-checks retrieved clinical knowledge and generated medical advice against verified medical gold-standards (e.g., PubMed, clinical guidelines) to systematically eliminate hallucinations and provide mathematically sound, auditable provenance for clinicians.

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📌 Research Vision & Core Concept

When standard LLMs use RAG for medical queries, they often suffer from source conflict, irrelevant retrieval, or synthesis hallucination. If a model synthesizes wrong dosage information, the consequences are critical. TrustMed-RAG solves this by implementing a dual-stage verification engine:

1. Retrieval Verification (Is the source correct?): Evaluates whether the fetched documents from the vector database are contextually sound, up-to-date, and free of contradictions.
2. Generation Verification (Is the answer faithful?): Probes the generated response against the retrieved source material using natural language inference (NLI) to ensure no ungrounded claims are made.

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🛠️ Key Features & Methodology

1. Dynamic Verification Engine: Implements customized NLI (Natural Language Inference) models and cross-encoders to measure semantic entailment between questions, sources, and answers.
2. Clinical Vector Store Pipeline: Pre-configured architectures using Qdrant/FAISS vectorized over open medical repositories like PubMed Central and specialized clinical textbooks.
3. Self-Correction Feedback Loop: If a generated answer fails the verification threshold, the framework automatically triggers a re-querying or re-generation cycle.
4. Fidelity-Latency Tradeoff Evaluation: Tools to benchmark the rigorousness of verification checks against real-time operational inference speeds.

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📂 Repository Structure

├── src/
│   ├── ingestion/          # Data parsing, chunking, and medical vector store indexing
│   ├── retrieval/          # Semantic search, re-ranking, and source conflict detection
│   ├── verification/       # Dual-stage verification models (NLI hooks, source cross-encoders)
│   └── generation/         # Self-correcting generation loops and prompt orchestrators
├── data/                   # Benchmark loaders for MedQA, PubMedQA, and BioASQ
├── configs/                # Vector dimensions, similarity metrics, and verification thresholds
├── notebooks/              # Citation mapping graphs, hallucination tracking, and error analysis
├── Literature_Review/      # Team research matrices and BibTeX reference files
└── README.md