Cardiac magnetic resonance imaging-large language model Meta AI: a finetuned large language model for identifying findings and associated attributes in cardiac magnetic resonance imaging reports.

ABSTRACT:

Background

Cardiac magnetic resonance imaging (CMR) studies contain a wealth of information on a patient's cardiovascular status. The ability to extract this data from free-text reports could serve to automate clinical decision support tools and generate data for retrospective clinical knowledge discovery, and clinical operational purposes. Few studies have examined the automatic extraction of data from free-text CMR reports, and the existing studies that do have key limitations, including small sample size and disease-specific data extraction. Existing studies also fail to extract features associated with the cardiovascular conditions that reflect nuances in natural language, such as uncertainty, severity, subtype, and anatomical locations of the condition. The goal of this study

SUBMITTER: Fang MZ 

PROVIDER: S-EPMC12766592 | biostudies-literature | 2025 Winter

REPOSITORIES: biostudies-literature