Abbaker Namariq, Minervini Fabrizio, Guttadauro Angelo, Solli Piergiorgio, Cioffi Ugo, Scarci Marco
Division of Thoracic Surgery, Imperial College NHS Healthcare Trust and National Heart and Lung Institute, London, United Kingdom.
Division of Thoracic Surgery, Luzerner Kantonsspital, Lucern, Switzerland.
Front Oncol. 2024 Feb 13;14:1347464. doi: 10.3389/fonc.2024.1347464. eCollection 2024.
To present a comprehensive review of the current state of artificial intelligence (AI) applications in lung cancer management, spanning the preoperative, intraoperative, and postoperative phases.
A review of the literature was conducted using PubMed, EMBASE and Cochrane, including relevant studies between 2002 and 2023 to identify the latest research on artificial intelligence and lung cancer.
While AI holds promise in managing lung cancer, challenges exist. In the preoperative phase, AI can improve diagnostics and predict biomarkers, particularly in cases with limited biopsy materials. During surgery, AI provides real-time guidance. Postoperatively, AI assists in pathology assessment and predictive modeling. Challenges include interpretability issues, training limitations affecting model use and AI's ineffectiveness beyond classification. Overfitting and global generalization, along with high computational costs and ethical frameworks, pose hurdles. Addressing these challenges requires a careful approach, considering ethical, technical, and regulatory factors. Rigorous analysis, external validation, and a robust regulatory framework are crucial for responsible AI implementation in lung surgery, reflecting the evolving synergy between human expertise and technology.
全面综述人工智能（AI）在肺癌管理中的应用现状，涵盖术前、术中和术后阶段。
使用PubMed、EMBASE和Cochrane对文献进行综述，纳入2002年至2023年间的相关研究，以确定关于人工智能与肺癌的最新研究。
虽然人工智能在肺癌管理方面具有前景，但也存在挑战。在术前阶段，人工智能可改善诊断并预测生物标志物，尤其是在活检材料有限的情况下。在手术过程中，人工智能提供实时指导。术后，人工智能辅助病理评估和预测建模。挑战包括可解释性问题、影响模型使用的训练局限性以及人工智能在分类之外的无效性。过拟合和全局泛化，以及高计算成本和伦理框架构成障碍。应对这些挑战需要谨慎对待，考虑伦理、技术和监管因素。严格的分析、外部验证和强大的监管框架对于在肺手术中负责任地实施人工智能至关重要，这反映了人类专业知识与技术之间不断演变的协同作用。
