Ijlal Aisha, Mumtaz Hassan, Hassan Syed Muhammad, Mustafa Qurat-Ul-Ain, Khalil Ahmed Bazil Bin, Ali Umna, Tanveer Zainab Khayal, Sajjad Laiba
Jinnah Sindh Medical University, Karachi,Pakistan.
BPP University, London, UK.
Ann Med Surg (Lond). 2025 Apr 22;87(6):3566-3572. doi: 10.1097/MS9.0000000000003302. eCollection 2025 Jun.
Lung cancer remains the leading cause of cancer-related deaths globally, often detected in advanced stages with poor prognosis. While surgical resection is the mainstay of curative treatment, early detection remains a significant challenge. Advances in personalized medicine, including genomic profiling and low-dose CT scans, have led to more tailored therapies, offering improved outcomes. Integrating artificial intelligence (AI) and machine learning (ML) into oncology has the potential to revolutionize lung cancer management by enhancing early detection, improving treatment precision, and supporting surgical decision-making. AI-driven technologies, such as deep learning algorithms and predictive models, have demonstrated effectiveness in identifying lung nodules, predicting immunotherapy response, and reducing diagnostic errors. Additionally, AI-powered robotics have contributed to improved surgical precision and better patient recovery. However, the widespread adoption of AI in clinical practice faces challenges, including data standardization, ethical concerns, and the need for robust validation. This study explores the question: How can AI and ML optimize thoracic surgical oncology by improving early detection, enhancing surgical precision, and enabling personalized care? This review highlights the significance of AI and ML in thoracic surgery and oncology, discussing their current applications, limitations, and future potential to advance personalized cancer care and improve patient outcomes.
肺癌仍然是全球癌症相关死亡的主要原因，通常在晚期才被发现，预后较差。虽然手术切除是根治性治疗的主要手段，但早期检测仍然是一项重大挑战。个性化医疗的进展，包括基因组分析和低剂量CT扫描，已带来了更具针对性的治疗方法，改善了治疗效果。将人工智能（AI）和机器学习（ML）整合到肿瘤学中，有可能通过加强早期检测、提高治疗精度和支持手术决策，彻底改变肺癌的治疗管理。诸如深度学习算法和预测模型等人工智能驱动的技术，已在识别肺结节、预测免疫治疗反应以及减少诊断错误方面显示出有效性。此外，人工智能驱动的机器人技术有助于提高手术精度和促进患者更好地康复。然而，人工智能在临床实践中的广泛应用面临挑战，包括数据标准化、伦理问题以及进行有力验证的必要性。本研究探讨了这样一个问题：人工智能和机器学习如何通过改善早期检测、提高手术精度以及实现个性化护理来优化胸外科肿瘤学？这篇综述强调了人工智能和机器学习在胸外科手术和肿瘤学中的重要性，讨论了它们目前的应用、局限性以及推进个性化癌症护理和改善患者治疗效果的未来潜力。
