Large language models for patient education prior to interventional radiology procedures: a comparative study.

ABSTRACT:

Purpose

This study evaluates four large language models' (LLMs) ability to answer common patient questions preceding transarterial periarticular embolization (TAPE), computed tomography (CT)-guided high-dose-rate (HDR) brachytherapy, and bleomycin electrosclerotherapy (BEST). The goal is to evaluate their potential to enhance clinical workflows and patient comprehension, while also assessing associated risks.

Materials and methods

Thirty-five TAPE, 34 CT-HDR brachytherapy, and 36 BEST related questions were presented to ChatGPT-4o, DeepSeek-V3, OpenBioLLM-8b, and BioMistral-7b. The LLM-generated responses were independently assessed by two board-certified radiologists. Accuracy was rated on a 5-point Likert scale. Statistics compared LLM performance across question categorie

SUBMITTER: Levita B 

PROVIDER: S-EPMC12518724 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature