组学技术和人工智能 (AI) 方法的进步正在推动我们在肝病学的个性化诊断、预后和治疗策略方面取得进展。这篇综述全面概述了用于分析肝脏疾病组学数据的人工智能方法的现状。我们概述了各种肝脏疾病的不同组学水平的患病率，并对研究中使用的人工智能方法进行了分类。具体来说，我们强调转录组和基因组分析的主导地位，以及蛋白质组和甲基化组等其他水平的相对稀疏的探索，这代表了新见解的未开发潜力。癌症基因组图谱和国际癌症基因组联盟等公开数据库计划为肝细胞癌诊断和治疗的进步铺平了道路。然而，对于其他肝脏疾病来说，大型组学数据集的可用性仍然有限。此外，应用复杂的人工智能方法来处理多组学数据集的复杂性需要大量数据来训练和验证模型，并且在临床实用性上实现无偏差结果方面面临挑战。讨论了解决数据缺乏和利用机会的策略。鉴于慢性肝病在全球造成的巨大负担，必须建立多中心合作来生成大规模组学数据，以进行早期疾病识别和干预。探索先进的人工智能方法对于最大限度地发挥这些数据集的潜力并改进早期检测和个性化治疗策略也是必要的。© 作者（或其雇主）2024。在 CC BY-NC 下允许重复使用。禁止商业再利用。请参阅权利和权限。英国医学杂志出版。

Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.