Better understanding: can a large language model safely improve readability of patient information leaflets in interventional radiology?

ABSTRACT:

Objectives

This study aimed to evaluate the feasibility of using a large language model (LLM) to generate patient information leaflets (PILs) with improved readability based on PILs in the field of interventional radiology.

Methods

PILs were acquired from the Cardiovascular and Interventional Radiology Society of Europe website, reformatted, and uploaded to the GPT-4 user interface with a prompt aimed to simplify the language. Automated readability metrics were used to evaluate the readability of original and LLM-modified PILs. Factual accuracy was assessed by human evaluation from three consultant interventional radiologists using an agreed marking scheme.

Results

LLM-modified PILs had significantly lower mean reading grade (9.5±0.5) compared with original PILs (11.1

SUBMITTER: Clackett W 

PROVIDER: S-EPMC12506176 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature