Background/Objectives: This systematic literature review examines the application of Artificial Intelligence (AI) in the diagnosis, treatment, and follow-up of metastatic gastrointestinal cancers. Methods: The databases PubMed, Scopus, Embase (Ovid), and Google Scholar were searched for published articles in English from January 2010 to January 2022, focusing on AI models in metastatic gastrointestinal cancers. Results: forty-six studies were included in the final set of reviewed papers. The critical appraisal and data extraction followed the checklist for systematic reviews of prediction modeling studies. The risk of bias in the included papers was assessed using the prediction risk of bias assessment tool. Conclusions: AI techniques, including machine learning and deep learning models, have shown promise in improving diagnostic accuracy, predicting treatment outcomes, and identifying prognostic biomarkers. Despite these advancements, challenges persist, such as reliance on retrospective data, variability in imaging protocols, small sample sizes, and data preprocessing and model interpretability issues. These challenges limit the generalizability, clinical application, and integration of AI models.
背景/目的：本系统性文献综述旨在探讨人工智能在转移性胃肠道癌症的诊断、治疗及随访中的应用。方法：通过检索PubMed、Scopus、Embase（Ovid）及Google Scholar数据库，收集2010年1月至2022年1月期间发表的英文文献，重点关注人工智能模型在转移性胃肠道癌症领域的研究。结果：最终纳入46篇研究进行系统分析。采用预测模型研究系统综述清单进行质量评估与数据提取，并运用预测偏倚风险评估工具对纳入文献的偏倚风险进行评估。结论：人工智能技术（包括机器学习和深度学习模型）在提升诊断准确性、预测治疗效果及识别预后生物标志物方面展现出潜力。然而，当前研究仍面临诸多挑战，如对回顾性数据的依赖、影像学方案的差异性、样本量有限以及数据预处理与模型可解释性问题。这些因素制约了人工智能模型的普适性、临床应用及整合推广。
肿瘤(癌症)患者之家