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<p>Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases and remains a leading cause of cancer-related mortality globally. The biological heterogeneity of NSCLC challenges early detection, accurate staging, and optimal therapy. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled the integration of high-dimensional clinical, genomic, and imaging data, transforming cancer care. This narrative review critically examines recent AI/ML applications in NSCLC, emphasizing clinical validation, interpretability, and ethical deployment. We synthesize data from leading studies on imaging, genomics, and multimodal integration, highlighting how deep learning and ensemble methods have begun to outperform traditional diagnostic workflows. Persistent challenges, including dataset diversity, lack of external validation, interpretability, and ethical considerations must be addressed to realize translational impact. We advocate for large-scale, interdisciplinary collaboration to advance AI-powered personalized medicine and improve patient outcomes in NSCLC.</p>

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Artificial intelligence and machine learning in non-small cell lung cancer: the current state of the science on multi-omic applications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月9日

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  • Savy Nistala,
  • Julius Niyonzima,
  • Raunak Chahal,
  • Alina Hasan,
  • Evelyn Ho,
  • Arielle Janssens,
  • Leroy W. Wheeler,
  • Mark Nichols,
  • Saman Zeeshan

摘要

Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases and remains a leading cause of cancer-related mortality globally. The biological heterogeneity of NSCLC challenges early detection, accurate staging, and optimal therapy. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled the integration of high-dimensional clinical, genomic, and imaging data, transforming cancer care. This narrative review critically examines recent AI/ML applications in NSCLC, emphasizing clinical validation, interpretability, and ethical deployment. We synthesize data from leading studies on imaging, genomics, and multimodal integration, highlighting how deep learning and ensemble methods have begun to outperform traditional diagnostic workflows. Persistent challenges, including dataset diversity, lack of external validation, interpretability, and ethical considerations must be addressed to realize translational impact. We advocate for large-scale, interdisciplinary collaboration to advance AI-powered personalized medicine and improve patient outcomes in NSCLC.