Shen Xiaomin, Wu Jinxin, Su Junwei, Yao Zhenyu, Huang Wei, Zhang Li, Jiang Yiheng, Yu Wei, Li Zhao
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, China.
School of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Front Genet. 2023 Mar 7;14:1004481. doi: 10.3389/fgene.2023.1004481. eCollection 2023.
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.
肝细胞癌（HCC）是最常见的肝癌类型，发病率和死亡率都很高。HCC的传统诊断方法主要基于临床表现、影像学特征和组织病理学。随着人工智能（AI）的迅速发展，其在HCC的诊断、治疗和预后预测中的应用越来越广泛，一种用于HCC状态分类的自动化方法很有前景。AI整合标记的临床数据，根据相同类型的新数据进行训练，并执行解释任务。多项研究表明，AI技术可以帮助临床医生和放射科医生提高效率并降低误诊率。然而，AI技术的覆盖面导致在针对特定问题和情况选择哪种AI技术时存在困难。解决这一问题，可以显著减少确定所需医疗方法所需的时间，并针对不同问题提供更精确和个性化的解决方案。在我们对研究工作的综述中，我们总结了现有研究工作，并根据指定的数据、信息、知识、智慧（DIKW）框架对这些研究的主要结果进行比较和分类。
