Ding Guang-Yu, Shi Jie-Yi, Wang Xiao-Dong, Yan Bo, Liu Xi-Yang, Gao Qiang
Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Shanghai 200032, China.
Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Fudan University, Shanghai 200032, China.
ILIVER. 2024 Feb 8;3(1):100082. doi: 10.1016/j.iliver.2024.100082. eCollection 2024 Mar.
In recent years, significant advances have been achieved in liver cancer management with the development of artificial intelligence (AI). AI-based pathological analysis can extract crucial information from whole slide images to assist clinicians in all aspects from diagnosis to prognosis and molecular profiling. However, AI techniques have a "black box" nature, which means that interpretability is of utmost importance because it is key to ensuring the reliability of the methods and building trust among clinicians for actual clinical implementation. In this paper, we provide an overview of current technical advancements in the AI-based pathological analysis of liver cancer, and delve into the strategies used in recent studies to unravel the "black box" of AI's decision-making process.
近年来，随着人工智能（AI）的发展，肝癌管理取得了重大进展。基于AI的病理分析可以从全切片图像中提取关键信息，以协助临床医生在从诊断到预后及分子特征分析的各个方面做出决策。然而，AI技术具有“黑箱”性质，这意味着可解释性至关重要，因为它是确保方法可靠性以及在实际临床应用中建立临床医生信任的关键。在本文中，我们概述了基于AI的肝癌病理分析的当前技术进展，并深入探讨了近期研究中用于揭开AI决策过程“黑箱”的策略。
