FetchMerck_AI

A RAG-based clinical decision support system powered by the Merck Manuals FetchMerck_AI is a Retrieval-Augmented Generation (RAG) solution designed to help healthcare providers streamline clinical decision-making by surfacing relevant medical knowledge from the Merck Manuals in real time. The system retrieves contextually relevant passages from over 4,000 pages of medical reference content spanning 23 clinical sections, then generates grounded, citation-backed responses using a quantized Mistral-7B model.

Key Objectives

• Streamline clinical decision-making — Surface relevant diagnostic and treatment information at the point of care
• Analyze impact on diagnostics and patient outcomes — Evaluate how RAG-assisted retrieval affects clinical reasoning quality
• Standardize care practices — Leverage a trusted, evidence-based reference to reduce variation in clinical decisions
• Demonstrate feasibility — Provide a functional prototype showing real-world applicability of RAG in healthcare settings

Architecture

Component	Details
LLM	Mistral-7B-v0.1 (GGUF quantized)
Retrieval	RAG pipeline over vectorized Merck Manual content
Knowledge Base	Merck Manuals — 4,000+ page PDF covering 23 medical sections (disorders, diagnostics, drugs, tests)
Framework	LangChain + llama-cpp-python

How It Works

1. Document Ingestion — The Merck Manual PDF is chunked and embedded into a vector store
2. Query Processing — A provider's clinical question is embedded and matched against the knowledge base
3. Contextual Retrieval — The most relevant passages are retrieved with source attribution
4. Grounded Generation — Mistral-7B generates a response grounded in the retrieved evidence, reducing hallucination risk

About the Merck Manuals

The Merck Manuals are medical reference books published by the American pharmaceutical company Merck & Co. since 1899. They cover a comprehensive range of medical topics including disorders, tests, diagnoses, and drugs across 23 clinical sections. The manuals are widely regarded as one of the most trusted general medical references available.

Intended Use

• Primary users: Healthcare providers, clinical researchers, medical educators
• Use case: Point-of-care decision support, clinical education, care standardization research
• Setting: Research and prototyping — not intended for production clinical deployment without further validation

Limitations

• This is a research prototype demonstrating RAG feasibility in healthcare; it has not been validated for clinical production use
• Responses are grounded in the Merck Manual content and may not reflect the latest clinical guidelines or institution-specific protocols
• The system should augment — never replace — clinical judgment
• Performance depends on retrieval quality; edge cases or highly specialized queries may yield suboptimal results

Ethical Considerations

• Patient safety: This tool is designed as a decision support system, not an autonomous diagnostic agent
• Bias: The knowledge base reflects the scope and perspective of the Merck Manuals; providers should cross-reference with additional sources for complex cases
• Privacy: The system processes queries only — no patient data is stored or transmitted

Citation

If you use FetchMerck_AI in your research, please cite:

@misc{gracey2026fetchmerck,
title={FetchMerck_AI: RAG-Based Clinical Decision Support Using the Merck Manuals},
author={Gracey, Jeremy},
year={2026},
publisher={Hugging Face},
doi={10.57967/hf/8101},
url={https://huggingface.co/jeremygracey-ai/FetchMerck_AI}
}

Author

Jeremy Gracey — Clinical healthcare professional (8+ years) transitioning into healthcare AI/ML. Currently completing an AI/ML certificate at UT Austin McCombs School of Business.

• Hugging Face: @jeremygracey-ai
• Background: Anesthesia Technician & Psychiatric Technician → AI/ML Engineer
• Focus: Building AI systems that bridge the gap between clinical frontline experience and modern ML infrastructure