ReviewWorld journal of gastroenterology2025
Artificial intelligence applications for managing metabolic dysfunction-associated steatotic liver disease: Current status and future prospects.
Review in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Explainable Meta-Learning Ensemble Framework for Predicting Insulin Dose Adjustments in Diabetic Patients: A Comparative Machine Learning Approach with SHAP-Based Clinical Interpretability.Medicina (Kaunas, Lithuania) · 2026Article
- Pathological mechanisms and research progress of multilevel intervention therapy for coronary heart disease.Frontiers in immunology · 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
The incidence and prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) have continued to increase in recent years, making it one of the most common chronic liver diseases worldwide. MASLD is highly comorbid with obesity, type 2 diabetes, cardiovascular disease, and chronic kidney disease, posing a serious threat to public health and creating a significant medical and socioeconomic burden. Despite advances in research, current clinical practice still faces considerable challenges in early screening, risk stratification, prognostic prediction, and long-term therapeutic monitoring. Recent advances in artificial intelligence (AI) have provided transformative opportunities to address these challenges. AI has demonstrated unique advantages in imaging interpretation, multiomics integration, electronic health record analysis, and remote health management, significantly improving the accuracy and efficiency of the noninvasive diagnosis, individualized risk stratification, precision therapy, and dynamic disease monitoring of MASLD. In this mini-review, the latest advances in AI applications for MASLD diagnosis and management are systematically summarized, and a forward-looking perspective on the role of AI in enabling the next generation of smart health care systems for MASLD is offered, with the aim of providing theoretical and practical guidance for the clinical management of this disease.
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.