Artificial intelligence in imaging for liver disease diagnosis Article Swipe
Chenglong Yin
,
Huafeng Zhang
,
Jin Du
,
Yingling Zhu
,
Hongmei Zhu
,
Hongqin Yue
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fmed.2025.1591523
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fmed.2025.1591523
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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- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmed.2025.1591523
- https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1591523/pdf
- OA Status
- gold
- Cited By
- 9
- References
- 93
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409825008Canonical identifier for this work in OpenAlex
- DOI
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- Title
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Artificial intelligence in imaging for liver disease diagnosisWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-25Full publication date if available
- Authors
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Chenglong Yin, Huafeng Zhang, Jin Du, Yingling Zhu, Hongmei Zhu, Hongqin YueList of authors in order
- Landing page
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https://doi.org/10.3389/fmed.2025.1591523Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1591523/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1591523/pdfDirect OA link when available
- Concepts
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Medicine, Cirrhosis, Radiology, Magnetic resonance imaging, Hepatocellular carcinoma, Medical imaging, Elastography, Steatosis, Fatty liver, Liver disease, Liver biopsy, Grading (engineering), Chronic liver disease, Pathology, Disease, Ultrasound, Biopsy, Internal medicine, Engineering, Civil engineeringTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 9Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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