{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:49:50Z","timestamp":1787672990601,"version":"build-2736575974"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Radiol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>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. We expect that these research results will provide valuable references for guiding further research directions and promoting the effective application of AI in liver disease healthcare.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study used the Web of Science Core Collection database as the data source, searching the Science Citation Index Expanded (SCI-Expanded) and Current Chemical Reactions (CCR-Expanded) citation indexes. The search timeframe was set to include all relevant literature from 2010 to March 25, 2025. The research methodology adopted a multi-software joint analysis strategy: First, HistCite Pro 2.1 was used to analyze the historical evolution and citation relationships of literature in this field. The tables generated by the tool systematically recorded the development process of the literature, clearly depicting the evolution of the research field over time. Second, Scimago Graphica was used to create a country\/region collaboration network view, intuitively showing academic collaboration among countries\/regions (SCImago Lab, 2022). VOSviewer 1.6.20 was used to analyze collaboration networks and visualize keyword co-occurrences to identify main research themes and clusters. CiteSpace was used for deeper scientific literature analysis, precisely capturing the dynamic changes of research hotspots and the evolution of frontier trends through Burst Detection algorithms and Timezone View.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 655 papers were retrieved from 60 countries, 1462 research institutions, and 4,744 authors published in 279 journals. The number of papers surged dramatically during 2019\u20132024, 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 (magnetic resonance imaging, CT, and ultrasound), 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. Particularly, MRI-based liver fat quantification and fibrosis assessment, combined with deep learning technologies for non-invasive diagnostic methods, show potential to replace liver biopsy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fradi.2025.1634165","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T07:58:26Z","timestamp":1758009506000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["The evolution of artificial intelligence technology in non-alcoholic fatty liver 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