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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Related Experiment Video

Updated: Jan 11, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Large Language Models can Identify the Presence of MASH and Extract VCTE Measurements from Unstructured

Aryana T Far1, Aryan Ayati2, Jordan Guillot2

  • 1Division of Gastroenterology and Hepatology, Department of Medicine, University of California - San Francisco, San Francisco, CA, USA.

Digestive Diseases and Sciences
|November 8, 2025
PubMed
Summary

Large language models (LLMs) accurately extract Metabolic dysfunction-associated steatohepatitis (MASH) and Vibration Controlled Transient Elastography (VCTE) data from clinical notes. These LLM-extracted VCTE parameters predict patient outcomes, aiding liver disease research.

Keywords:
Clinical informaticsDigital phenotypingFibrosis assessmentNatural language processingRisk stratificationUnstructured clinical data

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Area of Science:

  • Artificial Intelligence in Medicine
  • Hepatology and Gastroenterology
  • Data Science in Clinical Research

Background:

  • Metabolic dysfunction-associated steatohepatitis (MASH) is a primary driver of cirrhosis.
  • Vibration Controlled Transient Elastography (VCTE) data, crucial for MASH assessment, is often trapped in unstructured clinical notes.
  • The utility of Large Language Models (LLMs) for extracting MASH and VCTE information remains largely unquantified.

Purpose of the Study:

  • To evaluate the accuracy and efficiency of GPT-4o and Claude 3.5 Sonnet in extracting MASH and VCTE (stiffness and CAP) measurements from clinical documentation.
  • To assess the cost-effectiveness of using LLMs for this data extraction task.
  • To explore the association of LLM-extracted VCTE features with clinical outcomes like death or decompensation.

Main Methods:

  • A cohort of 493 patients with compensated MASH cirrhosis was analyzed.
  • GPT-4o and Claude 3.5 Sonnet were compared for their ability to identify MASH and extract peak VCTE stiffness and Controlled Attenuation Parameter (CAP) values.
  • LASSO-Cox regression was employed for exploratory analysis linking extracted features to clinical endpoints.

Main Results:

  • GPT-4o and Claude 3.5 achieved high F1-scores for MASH identification (90.5% and 80.0%, respectively).
  • GPT-4o demonstrated superior accuracy in extracting peak VCTE stiffness (99.3%) and CAP (99.1%) compared to Claude 3.5 (93.3% and 94.1%).
  • LLM extraction costs were low (~$0.012-$0.014 per note), and extracted VCTE stiffness and CAP scores were significant predictors of death or decompensation.

Conclusions:

  • LLMs offer a highly accurate and cost-effective solution for extracting critical MASH and VCTE data from unstructured clinical text.
  • LLM-derived VCTE parameters are valuable predictors of clinical outcomes in patients with MASH cirrhosis.
  • Liver disease researchers are encouraged to integrate LLM-based methods to streamline real-world data analysis and advance clinical studies.