ReviewCancers2025
Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care.
Review in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Genetic modulators of metabolic dysfunction-associated steatotic liver disease (MASLD) and their epistatic interactions: from in vitro and animal models to clinical outcomes.BMC medical genomics · 2026Review
- MASLD biomarker discovery: evaluating lipidomics techniques across disease progression.Molecular biology reports · 2026Review
- Artificial Intelligence for Fibrosis Diagnosis in Metabolic-Dysfunction-Associated Steatotic Liver Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Biomarkers for early identification of metabolic dysfunction-associated steatotic liver disease (MASLD): a narrative review.Archives of medical science : AMS · 2026Article
- Clinical Trials and Translational Advances in Metabolic Targeting.Cancer treatment and research · 2026Review
- Artificial intelligence applications for managing metabolic dysfunction-associated steatotic liver disease: Current status and future prospects.World journal of gastroenterology · 2025Review
- [Research progress and future prospects for artificial intelligence in the diagnosis and treatment of fatty liver disease].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact.Journal of clinical medicine · 2025Review
- Roles of short-chain fatty acids in metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction-associated steatohepatitis.World journal of hepatology · 2025Review
- Association Between Visceral Adiposity and the Prediction of Hepatic Steatosis and Fibrosis in Patients with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD).Journal of clinical medicine · 2025Article
- Lights and Shadows of a Vegetarian Diet in Patients with Metabolic Dysfunction-Associated Steatotic Liver Disease.Nutrients · 2025Review
- Hidden in the Fat: Unpacking the Metabolic Tango Between Metabolic Dysfunction-Associated Steatotic Liver Disease and Metabolic Syndrome.International journal of molecular sciences · 2025Review
- Barriers and facilitators to non-pharmacological management of metabolic dysfunction-associated steatotic liver disease: a qualitative evidence synthesis.Frontiers in pharmacology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a leading cause of chronic liver disease. In recent years, artificial intelligence (AI) has attracted significant attention in healthcare, particularly in diagnostics, patient management, and drug development, demonstrating immense potential for application and implementation. In the field of MASLD, substantial research has explored the application of AI in various areas, including patient counseling, improved patient stratification, enhanced diagnostic accuracy, drug development, and prognosis prediction. However, the integration of AI in hepatology is not without challenges. Key issues include data management and privacy, algorithmic bias, and the risk of AI-generated inaccuracies, commonly referred to as "hallucinations". This review aims to provide a comprehensive overview of the applications of AI in hepatology, with a focus on MASLD, highlighting both its transformative potential and its inherent limitations.
Indexed as
Identifiers
What Socratic 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 Socratic graph.