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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Optimizing Retrieval-Augmented Generation (RAG) in clinical medicine: methods and performance evaluation.

Pengze Li¹, Anshum Patel², Sai Krishna Vallamchetla³

¹Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, United States.

Journal of the American Medical Informatics Association : JAMIA

|June 4, 2026

Summary

Retrieval-Augmented Generation (RAG) improves AI in sleep medicine, but its effectiveness hinges on data structure and retrieval methods. Structured data and hybrid retrieval are key for accurate AI diagnostics.

Keywords:

Retrieval-Augmented Generation information retrieval large language models natural language processing sleep medicine

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Artificial Intelligence in Medicine
Medical Informatics
Natural Language Processing

Background:

Large Language Models (LLMs) show promise for medical knowledge retrieval.
Optimizing Retrieval-Augmented Generation (RAG) architecture is crucial for accurate medical applications.
Sleep medicine presents unique challenges for AI due to specialized terminology and complex case data.

Purpose of the Study:

To evaluate the impact of RAG architecture components on LLM performance in sleep medicine.
To assess how corpus structure, retrieval strategy, and pipeline complexity influence medical problem-solving.
To benchmark open-source LLMs within a sleep medicine knowledge domain.

Main Methods:

Benchmarked four LLMs (Llama-3-8B, Llama-3-70B, Qwen 2.5-14B, Qwen 2.5-235B) using a sleep medicine textbook knowledge base.
Compared performance across corpus structures (raw text vs. table-of-contents aligned), retrieval strategies (dense vs. hybrid sparse-dense), and pipeline complexity (baseline vs. augmented).
Evaluated using multiple-choice question (MCQ) accuracy and clinical case vignette diagnostic ranking.

Main Results:

RAG enhanced MCQ accuracy for all tested LLMs, with gains up to 25.5% (Llama-8B: 61.8% to 72.4%, Qwen-235B: 87.3%).
Structured corpora improved primary diagnosis accuracy by 6.1% on average, with Qwen-235B showing a peak 10.2% increase.
Hybrid retrieval and structured data corrected context noise issues seen with dense retrieval on raw text, particularly for smaller models.

Conclusions:

RAG effectiveness is a balance between LLM size and data structure; smaller models benefit significantly from structured data.
Hybrid retrieval is essential for precision with specialized medical terms in sleep medicine.
A structured corpus with a baseline hybrid pipeline offers optimal stability and speed for clinical AI deployment.