ReviewFrontiers in medicine2025
Artificial intelligence in imaging for liver disease diagnosis.
Review in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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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
21 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
- An Attention-Residual Hybrid CNN for CT-Based Multiclass Classification of Alcohol-Related Liver Disease: Differential Diagnosis Against HBV-Related Cirrhosis.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence in hepatocellular carcinoma: imaging-based subtyping and prediction of treatment response and prognosis.Abdominal radiology (New York) · 2026Review
- AI-powered ultrasound radiofrequency analysis for non-invasive pediatric liver fat quantification.Scientific reports · 2026Article
- Artificial Intelligence for Fibrosis Diagnosis in Metabolic-Dysfunction-Associated Steatotic Liver Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- From prediction to practice: artificial intelligence as an enabling layer in liver transplant care - reflections on ILTS 2025.Frontiers in transplantation · 2026Article
- Interpretable Machine Learning Models for Early Detection of Metabolic Dysfunction-Associated Steatotic Liver Disease Using Non-Invasive Routine Clinical and Laboratory Data.International journal of general medicine · 2026Article
- 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
- Chronic liver disease detection using deep convolutional neural networks with MRI data: a deep learning approach.Polish journal of radiology · 2026Article
- Artificial Intelligence and Radiomics in Primary Liver Cancer Imaging: A Bibliometric and Visualized Analysis.Journal of hepatocellular carcinoma · 2026Article
- The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.Frontiers in immunology · 2026Review
- [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
- Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models.World journal of gastroenterology · 2025Review
- Review
- Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment.World journal of gastroenterology · 2025Review
- Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): Mechanisms, Clinical Implications and Therapeutic Advances.Endocrinology, diabetes & metabolism · 2025Review
- Beyond the BMI Paradox: Unraveling the Cellular and Molecular Determinants of Metabolic Health in Obesity.Biomolecules · 2025Review
- ChatGPT-4-Driven Liver Ultrasound Radiomics Analysis: Diagnostic Value and Drawbacks in a Comparative Study.JMIR AI · 2025Article
- Global, regional and national burden of Metabolic dysfunction-associated steatotic liver disease in adolescents and adults aged 15-49 years from 1990 to 2021: results from the 2021 Global Burden of Disease study.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liver diseases, including hepatitis, non-alcoholic fatty liver disease (NAFLD), cirrhosis, and hepatocellular carcinoma (HCC), remain a major global health concern, with early and accurate diagnosis being essential for effective management. Imaging modalities such as ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI) play a crucial role in non-invasive diagnosis, but their sensitivity and diagnostic accuracy can be limited. Recent advancements in artificial intelligence (AI) have improved imaging-based liver disease assessment by enhancing pattern recognition, automating fibrosis and steatosis quantification, and aiding in HCC detection. AI-driven imaging techniques have shown promise in fibrosis staging through US, CT, MRI, and elastography, reducing the reliance on invasive liver biopsy. For liver steatosis, AI-assisted imaging methods have improved sensitivity and grading consistency, while in HCC detection and characterization, AI models have enhanced lesion identification, classification, and risk stratification across imaging modalities. The growing integration of AI into liver imaging is reshaping diagnostic workflows and has the potential to improve accuracy, efficiency, and clinical decision-making. This review provides an overview of AI applications in liver imaging, focusing on their clinical utility and implications for the future of liver disease diagnosis.
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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.