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March 15, 2026Black Sea Journal of Engineering and ScienceOpen Access

Dissecting Medical RAG: Why Reranking Matters More Than Complexity in Question Answering

Hakan Emekci Daniel Quillan Roxas

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Key Points

The research aims to analyze the importance of reranking versus complexity in retrieval-augmented generation systems for medical question answering.
Designed a hierarchical RAG architecture with six core components.
Conducted systematic ablation studies on seven configurations.
Utilized the MedQA benchmark with 476 medical questions.
Evaluated configurations using GPT-4o mini across four key metrics.
The full system achieved an overall score of 3.64 out of 5.0.
Reranking removal led to a significant drop of -0.24 in overall performance.
Statistical validation utilized paired t-tests with effect size calculations.

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Implication

Evaluates the effectiveness of reranking in medical question answering systems, suggesting it outweighs complexity.

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Cite This Study

Emekci et al. (2026) studied this question.

synapsesocial.com/papers/69b8f11edeb47d591b8c5ff9 https://doi.org/10.34248/bsengineering.1849342

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Medical Question Answering System Based on Retrieval-augmented GenerationOriginal Research · 2026
2When does retrieval help medical question answering? A question-type sensitivity analysis and failure taxonomy for text-based (image-free) medical RAG, with an interpretable clinical filter (CAF-RAG)Original Research · 2026
3Retrieval-augmented generation for medical question answering: a multi-metric performance evaluationOriginal Research · 2026
4Benchmarking Retrieval-Augmented Generation for Medicine2024 · 24 citations
5Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical StudyOriginal Research · 2026

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