肝细胞癌是最常见的肝癌类型，具有较高的发病率和死亡率。传统的肝细胞癌诊断方法主要基于临床表现、影像特征和组织病理学。随着人工智能（AI）的快速发展，其越来越广泛地用于肝细胞癌的诊断、治疗和预后预测，自动化的肝细胞癌状态分类方法表现出了很大的前景。AI整合了标记的临床数据，训练相同类型的新数据并执行解释任务。一些研究表明，AI技术可以帮助临床医生和放射科医生更有效率地工作并降低误诊率。然而，AI技术的涵盖范围使得在特定问题和情况下选择哪种类型的AI技术变得困难。解决这个问题，可以显著减少确定所需医疗方法所需的时间并为不同问题提供更精确和个性化的解决方案。在我们的研究综述中，我们总结了现有的研究成果，根据DIKW框架对这些结果进行了比较和分类。版权所有©2023 Shen、Wu、Su、Yao、Huang、Zhang、Jiang、Yu和Li。

Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high morbidity and fatality rate. Traditional diagnostic methods for HCC are primarily based on clinical presentation, imaging features, and histopathology. With the rapid development of artificial intelligence (AI), which is increasingly used in the diagnosis, treatment, and prognosis prediction of HCC, an automated approach to HCC status classification is promising. AI integrates labeled clinical data, trains on new data of the same type, and performs interpretation tasks. Several studies have shown that AI techniques can help clinicians and radiologists be more efficient and reduce the misdiagnosis rate. However, the coverage of AI technologies leads to difficulty in which the type of AI technology is preferred to choose for a given problem and situation. Solving this concern, it can significantly reduce the time required to determine the required healthcare approach and provide more precise and personalized solutions for different problems. In our review of research work, we summarize existing research works, compare and classify the main results of these according to the specified data, information, knowledge, wisdom (DIKW) framework.Copyright © 2023 Shen, Wu, Su, Yao, Huang, Zhang, Jiang, Yu and Li.