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Related Experiment Video

Updated: May 26, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

Readability of Retina Patient Education Materials Generated With a Large Language Model.

Turner D Wibbelsman1,2, Martin Calotti1,2, Randy Calotti2

  • 1Retina Service, Wills Eye Hospital, Philadelphia, PA, USA.

Journal of Vitreoretinal Diseases
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) can simplify complex medical information for patients. While LLMs show promise in improving readability, expert-authored materials remain the gold standard for accuracy.

Keywords:
large language modelsreadabilityretina

Related Experiment Videos

Last Updated: May 26, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Patient education materials are crucial for understanding retinal conditions.
  • Assessing and improving the readability of medical information is essential for patient comprehension.
  • Large language models (LLMs) are emerging tools with potential applications in healthcare communication.

Purpose of the Study:

  • To evaluate the effectiveness of large language models (LLMs) in enhancing the readability of educational content for patients with retinal conditions.
  • To compare the reading levels of original patient education materials with those generated and modified by an LLM.

Main Methods:

  • Forty-one fact sheets on vitreoretinal conditions from the American Society of Retina Specialists (ASRS) were analyzed.
  • The multimodal LLM Generative Pre-trained Transformer 4 (GPT-4) was used to generate and simplify texts to a sixth-grade reading level.
  • Readability was assessed using the Average Reading Level Consensus Calc (ARLCalc) score, averaging 8 validated readability formulas.

Main Results:

  • Original ASRS fact sheets had an average reading level of 12.85.
  • GPT-4 generated responses averaged a reading level of 12.37.
  • GPT-4 enhanced texts achieved significantly lower average reading levels of 8.66 (GPT-4 Enhanced) and 9.37 (ASRS Enhanced), indicating improved readability (P < .001).

Conclusions:

  • Large language models (LLMs) demonstrate potential as tools to improve the readability of patient-facing medical texts.
  • LLM-generated content can be adjusted to meet lower reading level targets, aiding patient understanding.
  • Specialty-authored patient education materials continue to be the benchmark for medical information accuracy.