(1) Background: Lung cancer’s high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI’s performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis followed the PRISMA guidelines rigorously, outlining a comprehensive protocol and employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring the selection of high-quality data relevant to AI’s role in lung cancer detection. The extraction of key study details and performance metrics, followed by quality assessment, facilitated a robust analysis using R software (Version 4.3.0). The process, depicted via a PRISMA flow diagram, allowed for the meticulous evaluation and synthesis of the findings in this review. (3) Results: From 1024 records, 39 studies met the inclusion criteria, showcasing diverse AI model applications for lung cancer detection, emphasizing varying strengths among the studies. These findings underscore AI’s potential for early lung cancer diagnosis but highlight the need for standardization amidst study variations. The results demonstrate promising pooled sensitivity and specificity of 0.87, signifying AI’s accuracy in identifying true positives and negatives, despite the observed heterogeneity attributed to diverse study parameters. (4) Conclusions: AI demonstrates promise in early lung cancer detection, showing high accuracy levels in this systematic review. However, study variations underline the need for standardized protocols to fully leverage AI’s potential in revolutionizing early diagnosis, ultimately benefiting patients and healthcare professionals. As the field progresses, validated AI models from large-scale perspective studies will greatly benefit clinical practice and patient care in the future.
（1）背景：肺癌因诊断较晚而死亡率高，凸显了早期检测策略的必要性。人工智能在医疗健康领域的应用，特别是在肺癌方面，通过分析医疗数据以实现早期识别和个性化治疗，展现出巨大潜力。本系统综述评估了人工智能在早期肺癌检测中的表现，分析了其技术、优势、局限性以及相较于传统方法的比较优势。 （2）方法：本系统综述和荟萃分析严格遵循PRISMA指南，制定了全面的研究方案，并在多个数据库中采用了针对性的检索策略。两名评审员根据预设标准独立筛选研究，确保选取与人工智能在肺癌检测中作用相关的高质量数据。提取关键研究细节和性能指标后，进行质量评估，并使用R软件（版本4.3.0）进行了稳健的分析。通过PRISMA流程图展示的整个过程，实现了对本综述研究结果的细致评估与综合。 （3）结果：从1024条记录中，39项研究符合纳入标准，展示了多种人工智能模型在肺癌检测中的应用，并强调了各研究间的不同优势。这些发现凸显了人工智能在早期肺癌诊断中的潜力，但也指出在研究差异中实现标准化的必要性。结果显示，汇总敏感性和特异性均达到0.87，表明人工智能在识别真阳性和真阴性方面具有较高的准确性，尽管观察到的异质性可归因于研究参数的多样性。 （4）结论：人工智能在早期肺癌检测中展现出良好前景，本系统综述显示其具有较高的准确性水平。然而，研究间的差异凸显了标准化方案的必要性，以充分发挥人工智能在革新早期诊断方面的潜力，最终使患者和医疗专业人员受益。随着该领域的发展，来自大规模前瞻性研究的经过验证的人工智能模型，将在未来极大地促进临床实践和患者护理。
肿瘤(癌症)患者之家