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Retrieval-Augmented Generation Versus GPT-4o for Patient-Facing Gynecological Cancer Information: Quality Evaluation.

Stephen Pearson¹, Mimi Reyburn², Conor Foley³

¹Critical Care, University College London Hospitals NHS Foundation Trust, London, England, United Kingdom.

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|April 24, 2026

Summary

This summary is machine-generated.

Retrieval-augmented generation enhanced patient-facing gynecological cancer information quality. This AI approach improved scores primarily by providing better source attribution for medical content.

Keywords:

GPT-4o gynecological neoplasms health literacy large language models patient education as topic readability retrieval-augmented generation

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Area of Science:

Artificial intelligence in medicine
Medical informatics
Natural language processing

Background:

Patient-facing health information requires accuracy and clarity.
Gynecological cancer information needs reliable sourcing.
Traditional content generation may lack robust source attribution.

Purpose of the Study:

To evaluate the impact of retrieval-augmented generation (RAG) on patient-facing gynecological cancer information.
To assess improvements in information quality and source attribution using RAG.

Main Methods:

Implementing retrieval-augmented generation (RAG) for content creation.
Comparing RAG-generated content with existing information sources.
Utilizing quality scoring metrics focused on accuracy and attribution.

Main Results:

Retrieval-augmented generation significantly improved overall quality scores for gynecological cancer information.
Enhanced source attribution was the primary driver of quality improvements.
Patient-facing materials demonstrated greater trustworthiness and clarity.

Conclusions:

RAG is a promising method for enhancing the quality of patient-facing medical information.
Improved source attribution via RAG boosts confidence in health content.
This technology can aid in delivering more reliable gynecological cancer patient education.