ReviewWorld journal of hepatology2025
Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Machine learning for non-invasive diagnosis and risk stratification.
Review in World journal of hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Artificial Intelligence in Surveillance of Hepatocellular Carcinoma: Hype or Reality?Journal of gastroenterology and hepatology · 2026Article
- Imaging-based fibrosis assessment and risk stratification in MASLD.Frontiers in medicine · 2026Review
- AI-driven diagnostic and prognostic models for metabolic dysfunction-associated steatotic liver disease: insights from clinical, imaging, and multi-omics studies-a scoping review.Frontiers in medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease, represents a growing global health burden, contributing significantly to liver-related morbidity and mortality. Early detection and timely intervention are essential to prevent disease progression. Conventional diagnostic methods, which rely on specialized imaging and invasive liver biopsies, underscore the need for non-invasive, cost-effective alternatives. Artificial intelligence-particularly machine learning and deep learning-has emerged as a transformative tool in MASLD diagnostics, offering improved accuracy in risk prediction, imaging interpretation, and disease stratification. This review synthesizes recent advancements in AI-based MASLD diagnostics, highlighting key models, performance metrics, and clinical applications, while addressing ongoing challenges such as data standardization, interpretability, and clinical validation.
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.