Utilizing a domain-specific large language model for LI-RADS v2018 categorization of free-text MRI reports: a feasibility study.

ABSTRACT:

Objective

To develop a domain-specific large language model (LLM) for LI-RADS v2018 categorization of hepatic observations based on free-text descriptions extracted from MRI reports.

Material and methods

This retrospective study included 291 small liver observations, divided into training (n = 141), validation (n = 30), and test (n = 120) datasets. Of these, 120 were fictitious, and 171 were extracted from 175 MRI reports from a single institution. The algorithm's performance was compared to two independent radiologists and one hepatologist in a human replacement scenario, and considering two combined strategies (double reading with arbitration and triage). Agreement on LI-RADS category and dichotomic malignancy (LR-4, LR-5, and LR-M) were estimated using linear-weighted κ s

SUBMITTER: Matute-Gonzalez M 

PROVIDER: S-EPMC11584817 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature