ReviewWorld journal of gastroenterology2026
Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.
Review in World journal of gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Development and validation of a machine learning-based model for identifying liver fibrosis in individuals with prior Schistosoma japonicum infection: a step toward precision management.Infectious diseases of poverty · 2026Article
- Multi-modal AI-enabled steatotic liver disease diagnostics using facial images and metabolomics.Cell reports. Medicine · 2026Article
- Enhanced Liver Fibrosis (ELF): From Prediction to Precision in MASLD.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Review
- Prediction of trajectories and outcomes in early-stage metabolic dysfunction-associated steatotic liver disease: a narrative review.EClinicalMedicine · 2026Review
- Risk Assessment and Prediction of Hepatocellular Carcinoma in Noncirrhotic Metabolic Dysfunction-Associated Steatotic Liver Disease.International journal of molecular sciences · 2026Review
- Imaging-based fibrosis assessment and risk stratification in MASLD.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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events. Given its significant clinical impact, the development of new strategies for early diagnosis and treatment is essential to improve patient outcomes. Over the past decade, the integration of artificial intelligence (AI) into gastroenterology has led to transformative advancements in medical practice. AI represents a major step towards personalized medicine, offering the potential to enhance diagnostic accuracy, refine prognostic assessments, and optimize treatment strategies. Its applications are rapidly expanding. This article explores the emerging role of AI in the management of MASLD, emphasizing its ability to improve clinical prediction, enhance the diagnostic performance of imaging modalities, and support histopathological confirmation. Additionally, it examines the development of AI-guided personalized treatments, where lifestyle modifications and close monitoring play a pivotal role in achieving therapeutic success.
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.