摘 要
人工智能（AI）作为当今科技领域的热点，其定义涵盖了模拟、延伸和扩展人类智能的技术与系统。AI的关键技术包括机器学习、深度学习、自然语言处理和计算机视觉等，这些技术共同构成了AI的核心能力。AI的特点在于其自适应性、学习性和高效性，能够处理海量数据并作出准确判断。在肺癌CT影像诊断中，AI的应用展现出巨大潜力。通过肺结节的自动检测与分类，AI能够显著提高诊断的准确性和效率。同时，AI还能预测肺癌的病理类型和分期，为治疗方案的制定提供有力支持。此外，AI还能辅助制定治疗计划并评估治疗效果，进一步提高了肺癌治疗的效果。这些应用不仅提高了诊断效率，还有效降低了漏诊率。然而，AI在肺癌CT影像诊断中也面临一些挑战。数据异质性、算法可靠性不足、数据泄露风险以及临床接受有限等问题限制了AI的广泛应用。为解决这些问题，需要采取一系列应对策略，如数据标准化、模型更新与优化、数据加密以及医生教育与培训等。通过这些措施，可以进一步推动AI在肺癌CT影像诊断领域的应用和发展，为患者带来更好的诊疗体验。
关键词：人工智能；肺癌诊断；应用研究
Abstract
Artificial intelligence (AI) is a hot spot in the field of science and technology today, and its definition covers the technologies and systems that simulate, extend and expand human intelligence. Key technologies in AI include machine learning, deep learning, natural language processing, and computer vision, which together constitute the core competencies of AI. AI is characterized by its adaptability, learning and efficiency, and its ability to process massive data and make accurate judgments. In the CT imaging diagnosis of lung cancer, the application of AI shows great potential. Through the automatic detection and classification of pulmonary nodules, AI can significantly improve the accuracy and efficiency of diagnosis. At the same time, AI can also predict the pathological type and stage of lung cancer, providing strong support for the formulation of treatment plan. In addition, AI can also assist in the formulation of treatment planning and evaluation of treatment effects, further improving the effect of lung cancer treatment. These applications not only improve the diagnostic efficiency, but also effectively reduce the missed diagnosis rate. However, AI also faces some challenges in the diagnosis of CT imaging of lung cancer. Data heterogeneity, insufficient algorithm reliability, risk of data leakage, and limited clinical acceptance limit the wide application of AI. To solve these problems, a series of coping strategies are needed, such as data standardization, model update and optimization, data encryption, and doctor education and training. Through these measures, the application and development of AI in the field of lung cancer CT imaging diagnosis can be further promoted, so as to bring better diagnosis and treatment experience to patients.
Keywords：Artificial intelligence; Diagnosis of lung cancer; Applied research
目 录
引 言 1
第一章 人工智能技术概述 3
1.1 人工智能的定义 3
1.2 人工智能的关键技术 3
1.3 人工智能的特点 4
第二章 人工智能在肺癌CT影像诊断中的应用 5
2.1 肺结节自动检测与分类 5
2.1.1 自动检测 5
2.1.2 分类识别 5
2.2 肺癌病理类型与分期预测 6
2.2.1 病理类型预测 6
2.2.2 分期预测 6
2.3 治疗计划辅助与效果评估 7
2.3.1 治疗计划辅助 7
2.3.2 效果评估 8
2.4 提高诊断效率和降低漏诊率 8
2.4.1 诊断效率提升 8
2.4.2 漏诊率降低 9
第三章 人工智能在肺癌CT影像诊断中面临的挑战 10
3.1 数据异质性 10
3.2 算法可靠性不足 10
3.3 数据泄露风险 10
3.4 临床接受有限 11
第四章 人工智能在肺癌CT影像诊断中的应对策略 12
4.1 数据标准化 12
4.2 模型更新与优化 12
4.3 数据加密 12
4.4 医生教育与培训 13
结 论 14
参考文献 15
致 谢 16
