Large Language Models can Identify the Presence of MASH and Extract VCTE Measurements from Unstructured Documentation

LLMs can extract MASH presence and VCTE parameters from documentation with high accuracy and low cost. When incorporated into survival analyses, LLM-extracted variables are associated with important clinical outcomes. Given the growing availability of LLMs, liver diseases researchers should incorporate these methods to facilitate real-world studies.

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Publication Details

Journal
Digestive Diseases and Sciences
Published
2025-11-08
DOI
https://doi.org/10.1007/s10620-025-09539-1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Large Language Models can Identify the Presence of MASH and Extract VCTE Measurements from Unstructured Documentation

Aryan Ayati, Jordan Guillot, Vivek A. Rudrapatna, Ge Jin et al.
Digestive Diseases and Sciences
Radiomics and Machine Learning in Medical Imaging
article

Large Language Models can Identify the Presence of MASH and Extract VCTE Measurements from Unstructured Documentation

Aryan Ayati, Jordan Guillot, Vivek A. Rudrapatna, Ge Jin, Aryana T. Far, Shadera Azzam
article en

Abstract

LLMs can extract MASH presence and VCTE parameters from documentation with high accuracy and low cost. When incorporated into survival analyses, LLM-extracted variables are associated with important clinical outcomes. Given the growing availability of LLMs, liver diseases researchers should incorporate these methods to facilitate real-world studies.

Digestive Diseases and Sciences
University of California, San Francisco (US)
Openalex Percentile: Top 52%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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Large Language Models can Identify the Presence of MASH and Extract VCTE Measurements from Unstructured Documentation — Aryan Ayati, Jordan Guillot, et al. · Digestive Diseases and Sciences (2025) | TGRS Research Map | TGRS