Automated cardiac magnetic resonance interpretation derived from prompted large language models.

ABSTRACT:

Background

The versatility of cardiac magnetic resonance (CMR) leads to complex and time-consuming interpretation. Large language models (LLMs) present transformative potential for automated CMR interpretations. We explored the ability of LLMs in the automated classification and diagnosis of CMR reports for three common cardiac diseases: myocardial infarction (MI), dilated cardiomyopathy (DCM), and hypertrophic cardiomyopathy (HCM).

Methods

This retrospective study enrolled CMR reports of consecutive patients from January 2015 to July 2024, including reports from three types of cardiac diseases: MI, DCM, and HCM. Six LLMs, including GPT-3.5, GPT-4.0, Gemini-1.0, Gemini-1.5, PaLM, and LLaMA, were used to classify and diagnose the CMR reports. The results of the LLMs, with min

SUBMITTER: Wang L 

PROVIDER: S-EPMC12432601 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature