ReviewAnnals of hepatology
A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease.
Review in Annals of hepatology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- The Latest Therapeutic Targets and New Drug Research of Metabolic Dysfunction-Associated Steatotic Liver Disease.Clinical pharmacology and therapeutics · 2026Review
- Profiling of extracellular vesicles from primary hepatocytes, organoids, and mash patients identifies cell injury-specific signatures.Scientific reports · 2026Article
- The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.Frontiers in immunology · 2026Review
- Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact.Journal of clinical medicine · 2025Review
- Assessing Clinician Engagement With a Passive Clinical Decision Support System for Liver Fibrosis Risk Stratification in a Weight Management Clinic.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2025Article
- Unraveling MASLD: The Role of Gut Microbiota, Dietary Modulation, and AI-Driven Lifestyle Interventions.Nutrients · 2025Review
- Digital Pathology Tailored for Assessment of Liver Biopsies.Biomedicines · 2025Article
- Mapping artificial intelligence adoption in hepatology practice and research: challenges and opportunities in MENA region.Frontiers in medicine · 2025Article
- The evolving landscape of cardiometabolic diseases.EBioMedicine · 2024Article
- Bridging current knowledge gap: better primary colorectal cancer prevention in people living with metabolic dysfunction-associated steatotic liver.Internal and emergency medicine · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge. Artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national-level 'data commons' (SteatoSITE) as an exemplar, the opportunities, as well as the technical challenges of large-scale databases in MASLD research, are highlighted.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.