Current lung cancer screening guidelines in the United States fail to identify many individuals at risk of developing the disease. Additionally, existing healthcare infrastructure has been leveraged to establish IPN clinics, a promising approach to addressing the limitations of current screening guidelines. Early-stage lung cancer is frequently diagnosed because of the incidental detection of pulmonary nodules on clinically indicated chest CT scans, particularly in the absence of formal screening programs. While artificial intelligence (AI) systems for lung cancer detection have demonstrated significant advancements in medicine, their clinical validation in screening settings remains limited. This review will discuss the pivotal trials underpinning the United States Preventive Services Task Force (USPSTF) recommendations for lung cancer screening, which have shaped the current guidelines for at-risk populations. We will explore recent studies investigating the role of AI in enhancing lung cancer screening efforts, highlighting how AI has the potential to improve early detection, streamline workflows, and reduce false positives and negatives in screening processes. This review will present the lung cancer screening rates at our institution, with a specific focus on the validation and integration of AI-driven technologies into our established screening programs. Using AI algorithms, we have validated enhanced detection capabilities through retrospective analysis of historical patient data, demonstrating significant improvements in identifying high-risk individuals and early-stage malignancies. These AI models, validated through rigorous cross-validation methods and clinical trials, have proven to outperform traditional screening approaches in sensitivity and specificity. The integration of these AI technologies within the lung cancer screening framework not only optimizes existing programs but also expands access to screening, improving early detection rates and ultimately leading to better patient outcomes. Through continuous validation and refinement, we aim to solidify AI’s role in transforming lung cancer detection and patient care. Through ongoing validation and implementation, AI can play a crucial role in transforming lung cancer screening practices, ultimately contributing to earlier diagnosis and improved patient survival.
美国现行的肺癌筛查指南未能识别出许多有患病风险的个体。此外，现有医疗基础设施已被用于建立IPN诊所，这是应对当前筛查指南局限性的一种有前景的方法。早期肺癌常因临床指征性胸部CT扫描中偶然发现肺结节而被诊断，尤其是在缺乏正式筛查计划的情况下。尽管用于肺癌检测的人工智能（AI）系统已在医学领域取得显著进展，但其在筛查环境中的临床验证仍然有限。本综述将讨论支持美国预防服务工作组（USPSTF）肺癌筛查建议的关键试验，这些试验塑造了当前针对高危人群的筛查指南。我们将探讨近期研究AI在加强肺癌筛查工作中作用的研究，重点阐述AI如何有潜力改善早期检测、优化工作流程，并减少筛查过程中的假阳性和假阴性。本综述将展示我们机构的肺癌筛查率，特别关注AI驱动技术在我们现有筛查项目中的验证与整合。通过使用AI算法，我们通过对历史患者数据的回顾性分析验证了增强的检测能力，证明在识别高危个体和早期恶性肿瘤方面有显著改进。这些通过严格交叉验证方法和临床试验验证的AI模型，在敏感性和特异性方面已证明优于传统筛查方法。将这些AI技术整合到肺癌筛查框架中，不仅优化了现有项目，还扩大了筛查的可及性，提高了早期检出率，最终改善了患者预后。通过持续验证和完善，我们旨在巩固AI在改变肺癌检测和患者护理中的作用。通过持续的验证和实施，AI可以在改变肺癌筛查实践中发挥关键作用，最终有助于更早诊断并提高患者生存率。
肿瘤(癌症)患者之家