Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

肝移植（LT）是终末期肝病患者的一种挽救生命的治疗方法。LT 接受者的管理很复杂，主要是因为在制定适当的治疗计划时需要考虑人口统计、临床、实验室、病理、影像和组学数据。目前整理临床信息的方法容易受到一定程度的主观性影响；因此，LT 的临床决策可以受益于人工智能 (AI) 提供的数据驱动方法。机器学习和深度学习可以应用于 LT 之前和之后的环境。移植前人工智能应用的一些例子包括优化移植候选决策和供体-受体匹配，以降低候补死亡率并改善移植后结果。在 LT 后设置中，人工智能可以帮助指导 LT 受者的管理，特别是通过预测患者和移植物的存活率，以及识别疾病复发和其他相关并发症的风险因素。尽管人工智能在医学领域显示出前景，但其临床部署仍存在局限性，包括模型训练的数据集不平衡、数据隐私问题以及缺乏可用的研究实践来衡量现实世界中的模型性能。总体而言，人工智能工具有潜力增强个性化临床决策，特别是在肝移植医学领域。其临床部署存在局限性，包括模型训练的数据集不平衡、数据隐私问题以及缺乏可用的研究实践来衡量现实世界中的模型性能。总体而言，人工智能工具有潜力增强个性化临床决策，特别是在肝移植医学领域。其临床部署存在局限性，包括模型训练的数据集不平衡、数据隐私问题以及缺乏可用的研究实践来衡量现实世界中的模型性能。总体而言，人工智能工具有潜力增强个性化临床决策，特别是在肝移植医学领域。