ReviewZhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology2025
[Research progress and future prospects for artificial intelligence in the diagnosis and treatment of fatty liver disease].
Review in Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese 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, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- [Construction norms for the tiered diagnosis and treatment and standardized management center of metabolic-associated fatty liver disease].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Guideline
- [Efficacy and safety profile of magnesium isoglycyrrhizate in patients with a ≤3 fold- elevation of the upper limit of normal transaminases: a multicenter, real-world retrospective study].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Article
- [Advances in research on metabolic associated fatty liver disease in 2025].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 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
3 authors.
Funding
Abstract
The continuous advancement of artificial intelligence (AI)-related technologies is promoting a diagnosis and treatment model for fatty liver disease toward precision medicine. AI assistance reduces human error and enhances the accuracy of non-invasive diagnostics in the field of imaging. Additionally, AI can automatically identify and quantify key histological features when combined with multi-omics data in the field of pathology and can also build more efficient diagnostic and risk assessment models; meanwhile, it aids in personalized treatment decisions and outcome prediction. However, problems such as data heterogeneity, insufficient algorithm interpretability, and lack of multi-center validation remain to be solved, thus necessitating interdisciplinary and multi-center collaboration to promote the in-depth application of AI in the clinical practice of fatty liver disease. This review summarized the current research progress of AI in the diagnosis and treatment of fatty liver disease and discusses the existing issues and challenges.
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.