ArticleTherapeutic advances in gastroenterology2021
Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis.
Article in Therapeutic advances in gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07305324 (Validation of an AI Tool for Improving MASLD Advanced Liver Fibrosis Diagnosis in Primary Care), which is not on this map. Cited by 28 papers, 5 of them syntheses that pooled it.
What it found
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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.
Validation of an AI Tool for Improving MASLD Advanced Liver Fibrosis Diagnosis in Primary Care: A Provider-Level Crossover Randomized Controlled Trial Pilot
Who cites it
28 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Applications of machine learning in the diagnosis of non-alcoholic fatty liver disease: a systematic review and meta-analysis.BMC gastroenterology · 2026Pooled it
- Diagnostic accuracy of artificial intelligence models for imaging detection of hepatic steatosis through systematic review and meta analysis.Scientific reports · 2025Pooled it
- Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis.BMC medical imaging · 2023Pooled it
- Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review.Medicina (Kaunas, Lithuania) · 2023Pooled it
- Lipid accumulation product (LAP) index for the diagnosis of nonalcoholic fatty liver disease (NAFLD): a systematic review and meta-analysis.Lipids in health and disease · 2023Pooled it
- AI-Based Screening for At-Risk MASLD and Advanced Fibrosis in the US Population: A Cost-Effectiveness Analysis.Digestive diseases and sciences · 2026Article
- A conceptual agentic AI architecture for MASLD-associated significant fibrosis in primary care.PLOS digital health · 2026Article
- An Explainable AI Tool (FibroX) for Detecting Advanced Liver Fibrosis in Adults With Type 2 Diabetes: Protocol for a Pilot Crossover Trial.JMIR research protocols · 2026Article
- Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.Digestive diseases and sciences · 2026Review
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- Harnessing AI-driven approaches for detecting metabolic dysfunction-associated steatotic liver disease, assessing fibrosis, and stratifying hepatocellular carcinoma risk: a scoping review.Frontiers in oncology · 2026Review
- Development of a machine learning model to identify individuals with VCTE-derived at-risk MASH in a Spanish Mediterranean region.Frontiers in digital health · 2026Article
- The relationship between lipid accumulation products and non-alcoholic fatty liver disease among adults in the North of Iran: a cross-sectional study.BMC gastroenterology · 2025Article
- Diagnostic accuracy of convolutional neural networks in classifying hepatic steatosis from B-mode ultrasound images: a systematic review with meta-analysis and novel validation in a community setting in Telangana, India.The Lancet regional health. Southeast Asia · 2025Article
- Liver disease trends in the Asia-Pacific region for the next 50 years.Clinical and molecular hepatology · 2025Review
- Review
- Hidden in the Fat: Unpacking the Metabolic Tango Between Metabolic Dysfunction-Associated Steatotic Liver Disease and Metabolic Syndrome.International journal of molecular sciences · 2025Review
- Review
- Hybrid intelligent systems for liver disease prediction: a demographic-aware machine learning framework.Frontiers in medicine · 2025Article
- Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care.Bioengineering (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe global prevalence of non-alcoholic fatty liver disease (NAFLD) continues to rise. Non-invasive diagnostic modalities including ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy but with limited performance. Artificial intelligence (AI) is currently being integrated with conventional diagnostic methods in the hopes of performance improvements. We aimed to estimate the performance of AI-assisted systems for diagnosing NAFLD, non-alcoholic steatohepatitis (NASH), and liver fibrosis.
methodsA systematic review was performed to identify studies integrating AI in the diagnosis of NAFLD, NASH, and liver fibrosis. Pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and summary receiver operating characteristic curves were calculated.
resultsTwenty-five studies were included in the systematic review. Meta-analysis of 13 studies showed that AI significantly improved the diagnosis of NAFLD, NASH and liver fibrosis. AI-assisted ultrasonography had excellent performance for diagnosing NAFLD, with a sensitivity, specificity, PPV, NPV of 0.97 (95% confidence interval (CI): 0.91-0.99), 0.98 (95% CI: 0.89-1.00), 0.98 (95% CI: 0.93-1.00), and 0.95 (95% CI: 0.88-0.98), respectively. The performance of AI-assisted ultrasonography was better than AI-assisted clinical data sets for the identification of NAFLD, which provided a sensitivity, specificity, PPV, NPV of 0.75 (95% CI: 0.66-0.82), 0.82 (95% CI: 0.74-0.88), 0.75 (95% CI: 0.60-0.86), and 0.82 (0.74-0.87), respectively. The area under the curves were 0.98 and 0.85 for AI-assisted ultrasonography and AI-assisted clinical data sets, respectively. AI-integrated clinical data sets had a pooled sensitivity, specificity of 0.80 (95%CI: 0.75-0.85), 0.69 (95%CI: 0.53-0.82) for identifying NASH, as well as 0.99-1.00 and 0.76-1.00 for diagnosing liver fibrosis stage F1-F4, respectively.
conclusionAI-supported systems provide promising performance improvements for diagnosing NAFLD, NASH, and identifying liver fibrosis among NAFLD patients. Prospective trials with direct comparisons between AI-assisted modalities and conventional methods are warranted before real-world implementation. PROTOCOL REGISTRATION: PROSPERO (CRD42021230391).
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