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Related Experiment Video

Updated: Jan 28, 2026

Mouse Model of Metabolic Dysfunction-Associated Steatotic Liver Disease with Fibrosis
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Unrecognized Fibrosis Risk in MASLD: A Real-World Analysis and the Case for AI-Augmented Stratification.

Ruona Ebiai1, Jasmine McNair1, Sameera Shuaibi1

  • 1Department of Internal Medicine, Ochsner Clinic Foundation, New Orleans, Louisiana.

Gastro Hep Advances
|January 26, 2026
PubMed
Summary
This summary is machine-generated.

Significant care gaps exist in managing metabolic dysfunction-associated steatotic liver disease (MASLD) fibrosis risk. Artificial intelligence can improve MASLD detection and risk stratification, standardizing care and enhancing patient outcomes.

Keywords:
Artificial intelligenceFIB-4Fibrosis riskHepatic steatosisLiver diseaseMASLD

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Area of Science:

  • Hepatology
  • Medical Informatics
  • Public Health

Background:

  • Current metabolic dysfunction-associated steatotic liver disease (MASLD) fibrosis risk stratification relies on manual, provider-dependent processes.
  • This approach leads to variability in awareness, documentation, and follow-through, creating significant care gaps.
  • The study explores the potential of artificial intelligence (AI) to enhance MASLD detection and management.

Purpose of the Study:

  • To quantify care gaps in MASLD fibrosis risk stratification and management.
  • To investigate the role of AI in improving MASLD detection and management.

Main Methods:

  • Retrospective analysis of adult abdominal ultrasounds from 2024.
  • Natural language processing (NLP) identified hepatic steatosis; patients with cardiometabolic risk factors were included.
  • Fibrosis-4 (FIB-4) index calculated using age-adjusted thresholds to assess fibrosis risk; management defined by referral or documented follow-up.

Main Results:

  • 20.6% of 14,814 adults met MASLD criteria; 15.2% were high risk, 18.0% indeterminate, and 66.0% low risk for advanced fibrosis.
  • Significant referral gaps observed: 66.5% of high-risk and 58.7% of indeterminate-risk patients lacked appropriate referrals.
  • Overall, 28.0% of the MASLD cohort experienced critical, moderate, or monitoring care gaps.

Conclusions:

  • System-level coordination failures, not access barriers, drive MASLD management gaps.
  • AI-driven workflows integrated into EHRs can automate detection, risk calculation, and flagging of care gaps.
  • AI offers a scalable solution to standardize MASLD management and improve patient outcomes.