肝移植后的人工智能：比较模型性能的范围综述
Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.
摘要(中文译文)
绘制并描述人工智能（AI）在肝移植（LT）术后护理中的应用，总结现有比较性性能数据，并识别方法学与转化方面的差距。我们按照PRISMA-ScR指南开展了一项范围综述。对电子数据库进行了从建库至2025年4月1日的全面检索。纳入评估AI在肝移植术后阶段应用（模型开发、验证或实施）的原始研究。比较性研究定义为对同一任务中至少两种算法模型进行头对头评估并报告定量性能指标的研究。单模型研究保留用于证据图谱绘制，但单独分析。综述及其他非原始文献纳入用于背景图谱绘制。检索共获得3088条记录。去重后筛选2408条，评估191篇全文，最终纳入65项研究。其中52项报告了主要结局数据。临床预测研究（n=43）聚焦于移植物存活、排斥反应、纤维化、肿瘤复发、死亡率和复合结局。运营类研究（n=3）评估了早期预警或床旁决策支持系统，系统层面研究（n=6）考察了基准测试、供受体匹配、可解释性、公平性和跨领域建模。大多数研究为回顾性、单中心设计，内部验证常见报道，外部验证较少见。肝移植术后护理中的AI研究正在扩展，主要集中于临床预测。然而，外部验证有限、方法异质性大以及真实世界实施稀缺，制约了临床就绪度。需要标准化评估和前瞻性整合，以确定AI工具能否支持决策并改善移植后结局。
Abstract
To map and characterize artificial intelligence (AI) applications in post-liver transplantation (LT) care, summarize comparative performance where available, and identify methodological and translational gaps. We conducted a scoping review in accordance with PRISMA-ScR. A comprehensive search of electronic databases was performed from inception through 1 April 2025. We included primary studies evaluating AI applications in the post-LT period (model development, validation, or implementation). Comparative studies were defined as those reporting head-to-head evaluation of at least two algorithmic models for the same task with quantitative performance metrics. Single-model studies were retained for evidence mapping but analyzed separately. Reviews and the other non-primary literature were included for contextual mapping. The search yielded 3088 records. After deduplication, 2408 were screened, 191 full texts were assessed, and 65 studies were included. Of these, 52 reported primary outcome data. Clinical prediction studies ( = 43) focused on graft survival, rejection, fibrosis, oncologic recurrence, mortality, and composite outcomes. Operational studies ( = 3) evaluated early warning or bedside decision-support systems, and system-level studies ( = 6) examined benchmarking, donor-recipient matching, explainability, fairness, and cross-domain modeling. Most studies were retrospective and single-center, with internal validation commonly reported and external validation uncommon. AI research in post-LT care is expanding, with a predominant focus on clinical prediction. However, limited external validation, heterogeneous methods, and scarce real-world implementation constrain clinical readiness. Standardized evaluation and prospective integration are needed to determine whether AI tools can support decision-making and improve post-transplant outcomes.
如何引用
AMA
Ileana Lulic, Ivan Gornik, Jadranka Pavicic Saric, Dunja Rogic, Alberto Gallego, Laura Karla Bozic, et al. Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.. Journal of clinical medicine. 2026; doi:10.3390/jcm15041491.
APA
Ileana Lulic, Ivan Gornik, Jadranka Pavicic Saric, Dunja Rogic, Alberto Gallego, Laura Karla Bozic, et al (2026). Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.. Journal of clinical medicine. https://doi.org/10.3390/jcm15041491
GB/T 7714
Ileana Lulic, Ivan Gornik, Jadranka Pavicic Saric, Dunja Rogic, Alberto Gallego, Laura Karla Bozic, et al. Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.[J]. Journal of clinical medicine, 2026 doi:10.3390/jcm15041491.
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