Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the most formidable challenges in oncology, characterized by its late detection and poor prognosis. Artificial intelligence (AI) and machine learning (ML) are emerging as pivotal tools in revolutionizing PDAC care across various dimensions. Consequently, many studies have focused on using AI to improve the standard of PDAC care. This review article attempts to consolidate the literature from the past five years to identify high-impact, novel, and meaningful studies focusing on their transformative potential in PDAC management. Our analysis spans a broad spectrum of applications, including but not limited to patient risk stratification, early detection, and prediction of treatment outcomes, thereby highlighting AI’s potential role in enhancing the quality and precision of PDAC care. By categorizing the literature into discrete sections reflective of a patient’s journey from screening and diagnosis through treatment and survivorship, this review offers a comprehensive examination of AI-driven methodologies in addressing the multifaceted challenges of PDAC. Each study is summarized by explaining the dataset, ML model, evaluation metrics, and impact the study has on improving PDAC-related outcomes. We also discuss prevailing obstacles and limitations inherent in the application of AI within the PDAC context, offering insightful perspectives on potential future directions and innovations.
胰腺导管腺癌（PDAC）因其诊断晚、预后差，仍是肿瘤学领域最严峻的挑战之一。人工智能与机器学习技术正逐渐成为推动PDAC诊疗模式变革的关键工具，相关研究日益聚焦于如何利用AI提升PDAC诊疗水平。本文旨在整合近五年文献，筛选出具有高影响力、创新性且对PDAC诊疗体系具有变革潜力的重要研究。我们的分析涵盖广泛的应用场景，包括但不限于患者风险分层、早期诊断及治疗结果预测，从而揭示AI在提升PDAC诊疗质量与精准度方面的潜在价值。通过将文献按照患者从筛查诊断到治疗生存的全病程进行归类，本综述系统考察了AI驱动的方法学如何应对PDAC的多维度挑战。每项研究均从数据集构成、机器学习模型、评估指标及对PDAC临床结局的改善作用等方面进行总结。同时，我们探讨了当前AI在PDAC应用中存在的固有障碍与局限，并对未来可能的发展方向与创新路径提出前瞻性见解。
肿瘤(癌症)患者之家