Malik Sheza, Das Rishi, Thongtan Thanita, Thompson Kathryn, Dbouk Nader
Department of Internal Medicine, Rochester General Hospital, Rochester, NY 14621, USA.
Division of Digestive Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA.
J Clin Med. 2024 Dec 22;13(24):7833. doi: 10.3390/jcm13247833.
The integration of artificial intelligence (AI) into hepatology is revolutionizing the diagnosis and management of liver diseases amidst a rising global burden of conditions like metabolic-associated steatotic liver disease (MASLD). AI harnesses vast datasets and complex algorithms to enhance clinical decision making and patient outcomes. AI's applications in hepatology span a variety of conditions, including autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, MASLD, hepatitis B, and hepatocellular carcinoma. It enables early detection, predicts disease progression, and supports more precise treatment strategies. Despite its transformative potential, challenges remain, including data integration, algorithm transparency, and computational demands. This review examines the current state of AI in hepatology, exploring its applications, limitations, and the opportunities it presents to enhance liver health and care delivery.
在全球代谢相关脂肪性肝病（MASLD）等疾病负担不断上升的背景下，将人工智能（AI）整合到肝病学中正在彻底改变肝脏疾病的诊断和管理。人工智能利用大量数据集和复杂算法来改善临床决策并提高患者治疗效果。人工智能在肝病学中的应用涉及多种疾病，包括自身免疫性肝炎、原发性胆汁性胆管炎、原发性硬化性胆管炎、MASLD、乙型肝炎和肝细胞癌。它能够实现早期检测、预测疾病进展并支持更精确的治疗策略。尽管具有变革潜力，但仍存在挑战，包括数据整合、算法透明度和计算需求等。本文综述探讨了人工智能在肝病学中的现状，研究了其应用、局限性以及它为改善肝脏健康和医疗服务带来的机遇。
