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

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Streamlining systematic reviews with large language models using prompt engineering and retrieval augmented

Fouad Trad¹, Ryan Yammine², Jana Charafeddine³

¹Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon. fat10@mail.aub.edu.

BMC Medical Research Methodology

|May 10, 2025

Summary

This summary is machine-generated.

Large Language Models (LLMs) significantly improve systematic review (SR) efficiency by automating literature screening. An LLM-based system reduced screening time by 95.5% compared to manual methods and Rayyan, while maintaining a low false negative rate (FNR).

Keywords:

Large language models Prompt engineering Rayyan AI Retrieval-augmented generation Systematic reviews

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Area of Science:

Medical Informatics
Evidence-Based Medicine
Artificial Intelligence in Research

Background:

Systematic reviews (SRs) are crucial for evidence-based guidelines but involve time-intensive literature screening.
Large Language Models (LLMs) offer potential to accelerate SR processes.

Purpose of the Study:

To compare the efficiency of a commercial tool (Rayyan) and an in-house LLM-based system for automating SR literature screening.
To evaluate the performance metrics of both automated systems against manual screening.

Main Methods:

A completed SR on Vitamin D and falls (14,439 articles) was used for comparison.
Rayyan was trained on 2,000 articles, categorizing the rest.
An LLM system utilized prompt engineering for title/abstract screening and Retrieval-Augmented Generation (RAG) for full-text screening.

Main Results:

The LLM system achieved a 99.5% article exclusion rate (AER) and 100% negative predictive value (NPV), reducing manual screening time by 95.5% (25.5 hours total).
Rayyan, with a threshold of 'likely to exclude,' achieved 0% FNR and 50.7% AER, but increased screening time to 81.3 hours.
The LLM system successfully identified all relevant articles while significantly decreasing overall screening effort.

Conclusions:

LLM-based systems substantially enhance SR efficiency compared to manual screening and commercial tools like Rayyan.
The LLM approach maintains a low false negative rate, ensuring comprehensive inclusion of relevant studies.