ORIGINAL RESEARCH article
Front. Radiol.
Sec. Artificial Intelligence in Radiology
Volume 5 - 2025 | doi: 10.3389/fradi.2025.1634165
The Evolution of Artificial Intelligence Technology in Non-alcoholic Fatty Liver Disease (NAFLD)
Provisionally accepted
- 1Guangdong Pharmaceutical University, Guangzhou, China
- 2Guangdong Medical University, Zhanjiang, China
- 3Guangdong Geriatrics Institute，Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: The incidence of Non-alcoholic Fatty Liver Disease (NAFLD) continues to rise, becoming one of the major causes of chronic liver disease globally and posing significant challenges to healthcare systems worldwide. Artificial intelligence (AI) technology, as an emerging tool, is gradually being integrated into clinical practice for NAFLD, providing innovative approaches to improve diagnostic efficiency, personalized treatment plans, and disease prognosis assessment. However, current research remains fragmented, lacking systematic and comprehensive analysis. Objective: This study conducts a bibliometric analysis of artificial intelligence applications in Non-alcoholic Fatty Liver Disease (NAFLD), aiming to identify research trends, highlight key areas, and provide comprehensive and objective insights into the current state of research in this field. Results: A total of 655 papers were retrieved from 60 countries, 1462 research institutions, and 4744 authors published in 279 journals. The number of papers surged dramatically during 2019-2024, with papers from these six years accounting for approximately 83.8% (549/655) of the total. Country-level analysis showed that the United States and China are the major contributors to this field; journal analysis indicated that Scientific Reports and Diagnostics are the journals with the highest publication volumes. In-depth analysis of 655 publications revealed four major research clusters: non-invasive assessment methods for liver fibrosis, imaging-based diagnosis, disease progression prediction model construction, and biomarker screening genes. Recent research trends indicate that deep learning algorithms and multimodal data fusion have become research hotspots in AI applications for NAFLD diagnosis and treatment. Conclusion: This study comprehensively outlines the development trajectory and knowledge structure of artificial intelligence technology in NAFLD research through systematic bibliometric analysis. The findings suggest that although the field faces challenges such as data standardization and model interpretability, AI technology shows broad prospects in NAFLD disease management and risk prediction. Future research should focus on multimodal data fusion, clinical translation, and evaluation of practical application value to promote the realization of AI-assisted precision medicine for NAFLD. This study not only depicts the current landscape of artificial intelligence applications in NAFLD but also provides a reference basis for future development in this field.
Keywords: artificial intelligence, Non-alcoholic fatty liver disease, metabolic-associated fatty liver disease, Metabolic dysfunction-associated steatotic liver disease, bibliometric analysis, Hepatology, Liver disease
Received: 27 May 2025; Accepted: 28 Aug 2025.
Copyright: © 2025 He, Wu, Lin, He, Zhuo and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yingying Li, Guangdong Pharmaceutical University, Guangzhou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.