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World J Hepatol. Mar 27, 2026; 18(3): 116233
Published online Mar 27, 2026. doi: 10.4254/wjh.v18.i3.116233
Published online Mar 27, 2026. doi: 10.4254/wjh.v18.i3.116233
Advances in artificial intelligence for imaging-based diagnosis of hepatocellular carcinoma
Zi-Xiong Zhou, School of Economics and Management, Shanghai Institute of Technology, Shanghai 200235, China
Jia-Jia Xiao, Guangxi Vocational and Technical College, Nanning 530022, Guangxi Zhuang Autonomous Region, China
Zhong-Xing Ning, Department of Hypertension and Vascular Disease, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
Zhong-Xing Ning, Department of Cardiovascular Medicine, Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-sen University, Nanning 530022, Guangxi Zhuang Autonomous Region, China
Zhong-Xing Ning, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
Co-first authors: Zi-Xiong Zhou and Jia-Jia Xiao.
Author contributions: Zhou ZX, Xiao JJ and Ning ZX contributed to the manuscript writing, reviewing, and editing; Xiao JJ and Ning ZX participated in the formal analysis, conceptualization, project administration, and supervision of this manuscript. Zhou ZX and Xiao JJ contributed equally and are co-first authors.
Supported by School Level Project of Guangxi Vocational and Technical College, No. 231208.
Conflict-of-interest statement: The authors declared that there is no conflict of interest.
Corresponding author: Zhong-Xing Ning, Department of Hypertension and Vascular Disease, The First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan 2nd Road, Yuexiu District, Guangzhou 510080, Guangdong Province, China. 592220690@qq.com
Received: November 6, 2025
Revised: November 23, 2025
Accepted: January 7, 2026
Published online: March 27, 2026
Processing time: 140 Days and 18 Hours
Revised: November 23, 2025
Accepted: January 7, 2026
Published online: March 27, 2026
Processing time: 140 Days and 18 Hours
Core Tip
Core Tip: Artificial intelligence (AI) for image-guided hepatocellular carcinoma diagnosis improves sensitivity to small lesions, enables standardized quantitative readouts, and reduces reporting time, thereby complementing conventional radiology. Built on diverse imaging datasets and evolving model designs, AI has advanced across detection, segmentation, characterization, and response assessment, with early clinical uptake. Outstanding obstacles-cross-institution generalization and clinically meaningful explainability-must be resolved to achieve scalable, trustworthy deployment.