ArticleHepatology communications2025
Development of an AI algorithm for early identification of MASLD in the electronic health record.
Article in Hepatology communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predictive modeling and clinical decision tools for risk stratification in steatotic liver disease.Clinical and molecular hepatology · 2026Review
- A comprehensive narrative review of artificial intelligence use in the diagnosis and management of metabolic dysfunction-associated steatotic liver disease.Translational gastroenterology and hepatology · 2026Review
- Metabolic Dysfunction-Associated Steatotic Liver Disease Is Associated With Adverse Social Factors: An Analysis UsingGastro hep advances · 2026Article
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
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9 authors.
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Abstract
backgroundMetabolic dysfunction-associated steatotic liver disease (MASLD) affects ~30% of the US adult population, and its prevalence is increasing. Early-stage disease is often missed by clinicians as it is largely asymptomatic until cirrhosis develops. Recent advances in artificial intelligence (AI) have allowed for expanded electronic health record (EHR) identification of several diseases. We aimed to develop an algorithm that utilizes AI to identify patients with MASLD via a large-scale review within the EHR.
methodsWe created a natural language processing (NLP) algorithm that identifies patients with hepatic steatosis on abdominal imaging and selects for patients meeting MASLD criteria. Validation was carried out via manual review of monthly generated cohorts. The algorithm was then utilized to retrieve a MASLD cohort for initial analysis. Demographics were summarized, and additional variables pertaining to early MASLD management were analyzed.
resultsOur algorithm identified patients with MASLD with a positive predictive value (PPV) of over 93%. The NLP component of the algorithm identified hepatic steatosis in imaging reports with a PPV of up to 99.4% and excluded patients with alcohol use with a negative predictive value of ~95%. From a cohort spanning 6 months, 957 individuals with MASLD were identified by the algorithm, of which 14.6% (n=140) had a MASLD-related diagnosis code.
conclusionsWe developed an AI algorithm that can perform a large-scale review of electronic health records to identify patients with MASLD with >93% accuracy. Our initial analysis suggests that a substantial proportion of individuals meeting MASLD criteria do not yet carry a MASLD-related diagnosis. Our work can be adapted by other institutions to enhance the detection of MASLD and alcohol use patterns, allowing for targeted interventions to prevent disease progression and improve outcomes.
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