Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor–recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.

实体器官移植是对经过严格筛选的终末期器官疾病患者的一种挽救生命的治疗方法。在过去几十年取得巨大进步的同时，也出现了新的挑战。器官供需之间日益扩大的差距需要最佳的患者/供体选择和匹配。长期移植物和患者生存的改善需要数据驱动的诊断和移植后并发症的管理。移植中越来越丰富的临床、遗传、放射学和代谢数据导致人们对应用机器学习 (ML) 工具越来越感兴趣，这些工具可以揭示大型数据集中的隐藏模式。机器学习算法已应用于等候名单死亡率的预测建模、供体-受体匹配、生存预测、移植后并发症诊断和预测，旨在优化免疫抑制和管理。在这篇综述中，我们深入了解了 ML 在移植医学中的各种应用，为什么这些应用被用于评估特定的临床问题，以及 ML 改变移植受者护理的潜力。在对以下数据库进行全面搜索后选择了 36 篇文章：Ovid MEDLINE；Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations；奥维德基地；Cochrane 系统评价数据库（Ovid）；和 Cochrane 对照试验中央登记册 (Ovid)。总之，这些研究表明，ML 技术在改善移植受者的结果方面具有巨大潜力。未来的工作需要提高这些算法的可解释性，通过更大规模的外部验证确保通用性，