ChatGPT yields low accuracy in determining LI-RADS scores based on free-text and structured radiology reports in German language.

ABSTRACT:

Background

To investigate the feasibility of the large language model (LLM) ChatGPT for classifying liver lesions according to the Liver Imaging Reporting and Data System (LI-RADS) based on MRI reports, and to compare classification performance on structured vs. unstructured reports.

Methods

LI-RADS classifiable liver lesions were included from German written structured and unstructured MRI reports with report of size, location, and arterial phase contrast enhancement as minimum inclusion requirements. The findings sections of the reports were propagated to ChatGPT (GPT-3.5), which was instructed to determine LI-RADS scores for each classifiable liver lesion. Ground truth was established by two radiologists in consensus. Agreement between ground truth and ChatGPT was assesse

SUBMITTER: Fervers P 

PROVIDER: S-EPMC11257913 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature