Background/Objectives: As the primary cause of cancer-related death globally, lung cancer highlights the critical need for early identification, precise staging, and individualized treatment planning. By enabling automated diagnosis, staging, and prognostic evaluation, recent developments in artificial intelligence (AI) and machine learning (ML) have completely changed the treatment of lung cancer. The goal of this narrative review is to compile the most recent data on uses of AI and ML throughout the lung cancer care continuum. Methods: A comprehensive literature search was conducted across major scientific databases to identify peer-reviewed studies focused on AI-based imaging, detection, and prognostic modeling in lung cancer. Studies were categorized into three thematic domains: (1) detection and screening, (2) staging and diagnosis, and (3) risk prediction and prognosis. Results: Convolutional neural networks (CNNs), in particular, have shown significant sensitivity and specificity in nodule recognition, segmentation, and false-positive reduction. Radiomics-based models and other multimodal frameworks combining imaging and clinical data have great promise for forecasting treatment outcomes and survival rates. The accuracy of non-small-cell lung cancer (NSCLC) staging, lymph node evaluation, and malignancy classification were regularly improved by AI algorithms, frequently matching or exceeding radiologist performance. Conclusions: There are still issues with data heterogeneity, interpretability, repeatability, and clinical acceptability despite significant advancements. Standardized datasets, ethical AI implementation, and transparent model evaluation should be the top priorities for future initiatives. AI and ML have revolutionary potential for intelligent, personalized, and real-time lung cancer treatment by connecting computational innovation with precision oncology.
背景/目的：作为全球癌症相关死亡的主要原因，肺癌凸显了早期识别、精准分期和个体化治疗规划的迫切需求。人工智能和机器学习的最新进展通过实现自动化诊断、分期和预后评估，彻底改变了肺癌的治疗模式。本叙述性综述旨在系统梳理人工智能和机器学习在肺癌全程诊疗领域应用的最新证据。方法：通过检索主要科学数据库，筛选聚焦于肺癌人工智能影像分析、检测及预后建模的同行评议研究。研究归纳为三大主题领域：(1)检测与筛查，(2)分期与诊断，(3)风险预测与预后。结果：卷积神经网络在肺结节识别、分割及假阳性降低方面展现出显著的敏感性与特异性。基于放射组学的模型及融合影像与临床数据的多模态框架在预测治疗反应和生存率方面前景广阔。人工智能算法持续提升非小细胞肺癌分期、淋巴结评估及恶性程度分类的准确性，其性能常达到或超越放射科医师水平。结论：尽管取得显著进展，但仍面临数据异质性、可解释性、可重复性及临床接受度等挑战。未来研究应优先关注标准化数据集建设、符合伦理的人工智能实施及透明化模型评估。通过将计算创新与精准肿瘤学相结合，人工智能与机器学习有望为肺癌的智能化、个体化及实时化诊疗带来革命性突破。
肿瘤(癌症)患者之家