Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Hepatitis Research Center, National Taiwan University Hospital, Taipei, Taiwan.
J Gastroenterol Hepatol. 2021 Mar;36(3):569-580. doi: 10.1111/jgh.15415.
The advancement of investigation tools and electronic health records (EHR) enables a paradigm shift from guideline-specific therapy toward patient-specific precision medicine. The multiparametric and large detailed information necessitates novel analyses to explore the insight of diseases and to aid the diagnosis, monitoring, and outcome prediction. Artificial intelligence (AI), machine learning, and deep learning (DL) provide various models of supervised, or unsupervised algorithms, and sophisticated neural networks to generate predictive models more precisely than conventional ones. The data, application tasks, and algorithms are three key components in AI. Various data formats are available in daily clinical practice of hepatology, including radiological imaging, EHR, liver pathology, data from wearable devices, and multi-omics measurements. The images of abdominal ultrasonography, computed tomography, and magnetic resonance imaging can be used to predict liver fibrosis, cirrhosis, non-alcoholic fatty liver disease (NAFLD), and differentiation of benign tumors from hepatocellular carcinoma (HCC). Using EHR, the AI algorithms help predict the diagnosis and outcomes of liver cirrhosis, HCC, NAFLD, portal hypertension, varices, liver transplantation, and acute liver failure. AI helps to predict severity and patterns of fibrosis, steatosis, activity of NAFLD, and survival of HCC by using pathological data. Despite of these high potentials of AI application, data preparation, collection, quality, labeling, and sampling biases of data are major concerns. The selection, evaluation, and validation of algorithms, as well as real-world application of these AI models, are also challenging. Nevertheless, AI opens the new era of precision medicine in hepatology, which will change our future practice.
研究工具和电子健康记录 (EHR) 的进步使得从针对指南的治疗转向针对患者的精准医学成为可能。多参数和大量详细信息需要新的分析方法来探索疾病的洞察力，并帮助诊断、监测和预测结果。人工智能 (AI)、机器学习和深度学习 (DL) 提供了各种监督或无监督算法模型，以及复杂的神经网络，可以比传统方法更精确地生成预测模型。数据、应用任务和算法是人工智能的三个关键组成部分。在肝脏病学的日常临床实践中，有多种数据格式可用，包括放射影像学、EHR、肝脏病理学、可穿戴设备的数据和多组学测量。腹部超声、计算机断层扫描和磁共振成像的图像可用于预测肝纤维化、肝硬化、非酒精性脂肪性肝病 (NAFLD) 以及良性肿瘤与肝细胞癌 (HCC) 的鉴别。使用 EHR，人工智能算法有助于预测肝硬化、HCC、NAFLD、门静脉高压、静脉曲张、肝移植和急性肝衰竭的诊断和结果。人工智能可通过病理数据预测纤维化、脂肪变性、NAFLD 活动和 HCC 生存率的严重程度和模式。尽管人工智能应用具有这些高潜力，但数据准备、收集、质量、标记和采样偏差是主要关注点。算法的选择、评估和验证以及这些人工智能模型的实际应用也具有挑战性。然而，人工智能开启了肝脏病学精准医学的新时代，这将改变我们的未来实践。
