Background:Hepatopancreatobiliary (HPB) surgery is among the most complex domains in oncologic care, where decisions entail significant risk–benefit considerations. Artificial intelligence (AI) has emerged as a promising tool for improving individualized decision-making through enhanced risk stratification, complication prediction, and survival modeling. However, its role in HPB oncologic surgery has not been comprehensively assessed.Methods:This systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO ID: CRD420251114173. A comprehensive search across six databases was performed through 30 May 2025. Eligible studies evaluated AI applications in risk–benefit assessment in HPB cancer surgery. Inclusion criteria encompassed peer-reviewed, English-language studies involving human s ubjects. Two independent reviewers conducted study selection, data extraction, and quality appraisal.Results:Thirteen studies published between 2020 and 2024 met the inclusion criteria. Most studies employed retrospective designs with sample sizes ranging from small institutional cohorts to large national databases. AI models were developed for cancer risk prediction (n = 9), postoperative complication modeling (n = 4), and survival prediction (n = 3). Common algorithms included Random Forest, XGBoost, Decision Trees, Artificial Neural Networks, and Transformer-based models. While internal performance metrics were generally favorable, external validation was reported in only five studies, and calibration metrics were often lacking. Integration into clinical workflows was described in just two studies. No study addressed cost-effectiveness or patient perspectives. Overall risk of bias was moderate to high, primarily due to retrospective designs and incomplete reporting.Conclusions:AI demonstrates early promise in augmenting risk–benefit assessment for HPB oncologic surgery, particularly in predictive modeling. However, its clinical utility remains limited by methodological weaknesses and a lack of real-world integration. Future research should focus on prospective, multicenter validation, standardized reporting, clinical implementation, cost-effectiveness analysis, and the incorporation of patient-centered outcomes.
背景：肝胆胰外科是肿瘤治疗中最复杂的领域之一，其临床决策需权衡显著的风险与获益。人工智能通过提升风险分层、并发症预测和生存建模能力，已成为改善个体化决策的有前景工具。然而，其在肝胆胰肿瘤外科中的作用尚未得到系统评估。 方法：本系统综述遵循PRISMA指南，已在PROSPERO注册（ID: CRD420251114173）。截至2025年5月30日，对六个数据库进行了全面检索。纳入研究评估人工智能在肝胆胰肿瘤手术风险获益评估中的应用，入选标准包括经同行评审、涉及人类受试者的英文研究。由两名独立评审员完成研究筛选、数据提取和质量评价。 结果：共纳入2020年至2024年间发表的13项研究。多数研究采用回顾性设计，样本规模从机构队列到国家数据库不等。人工智能模型主要用于癌症风险预测（9项）、术后并发症建模（4项）和生存预测（3项）。常用算法包括随机森林、XGBoost、决策树、人工神经网络及基于Transformer的模型。虽然内部性能指标总体良好，但仅5项研究报告了外部验证，且普遍缺乏校准指标评估。仅2项研究描述了临床工作流整合。无研究涉及成本效益或患者视角分析。总体偏倚风险为中至高度，主要源于回顾性设计及报告不完整。 结论：人工智能在增强肝胆胰肿瘤外科风险获益评估方面展现出初步潜力，尤其在预测建模领域。但其临床应用仍受限于方法学缺陷和实际整合不足。未来研究应聚焦前瞻性多中心验证、标准化报告、临床实施、成本效益分析以及患者为中心结局指标的纳入。
肿瘤(癌症)患者之家