SynthesisReviews in endocrine & metabolic disorders2022
Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review.
Synthesis in Reviews in endocrine & metabolic disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline 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
- Artificial Intelligence, Real Results: AI Successfully Risk-Stratifies Patients with MASLD.Digestive diseases and sciences · 2026Article
- Hepatic Steatosis Severity Prediction in Nonobese Individuals: Machine Learning Model Development and Validation.Journal of medical Internet research · 2026Article
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- Article
- 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
- ChatGPT improves usability, effectiveness, scalability, interpretability and accessibility, in early diagnosis of metabolic dysfunction-associated fatty liver disease.BMC gastroenterology · 2025Article
- [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
- Roles of short-chain fatty acids in metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction-associated steatohepatitis.World journal of hepatology · 2025Review
- Prediction of Metabolic Dysfunction-Associated Steatotic Liver Disease via Advanced Machine Learning Among Chinese Han Population.Obesity surgery · 2025Article
- Trends in the applications of artificial intelligence in fatty liver diseases.Hepatology international · 2025Review
- 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
- Machine learning-based identification of biochemical markers to predict hepatic steatosis in patients at high metabolic risk.World journal of gastroenterology · 2025Article
- FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD.BMC medical imaging · 2025Article
- Hidden in the Fat: Unpacking the Metabolic Tango Between Metabolic Dysfunction-Associated Steatotic Liver Disease and Metabolic Syndrome.International journal of molecular sciences · 2025Review
- Establishing predictive models for malignant and inflammatory pulmonary nodules using clinical data and CT imaging features.Quantitative imaging in medicine and surgery · 2025Article
- Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care.Cancers · 2025Review
- Global Trends in Non-Invasive Techniques for the Diagnosis and Monitoring of Nonalcoholic Fatty Liver Disease: A Bibliometric and Visualization Analysis.Journal of multidisciplinary healthcare · 2025Review
- Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-alcoholic fatty liver disease (NAFLD) is one of the most important causes of chronic liver disease in the world, it has been found that cardiovascular and renal risks and diseases are also highly prevalent in adults with NAFLD. Diagnosis and treatment of NAFLD face many challenges, although the medical science has been very developed. Efficiency, accuracy and individualization are the main goals to be solved. Evaluation of the severity of NAFLD involves a variety of clinical parameters, how to optimize non-invasive evaluation methods is a necessary issue that needs to be discussed in this field. Artificial intelligence (AI) has become increasingly widespread in healthcare applications, and it has been also brought many new insights into better analyzing chronic liver disease, including NAFLD. This paper reviewed AI related researches in NAFLD field published recently, summarized diagnostic models based on electronic health record and lab test, ultrasound and radio imaging, and liver histopathological data, described the application of therapeutic models in personalized lifestyle guidance and the development of drugs for NAFLD. In addition, we also analyzed present AI models in distinguishing healthy VS NAFLD/NASH, and fibrosis VS non-fibrosis in the evaluation of NAFLD progression. We hope to provide alternative directions for the future research.
Indexed as
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34396467What 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.