Opportunities and challenges in lung cancer care in the era of large language models and vision language models.

Journal: Translational lung cancer research
Published Date: May 30, 2025

Abstract

Lung cancer remains the leading cause of cancer-related deaths globally. Over the past decade, the development of artificial intelligence (AI) has significantly propelled lung cancer care, particularly in areas such as lung cancer early diagnosis, survival prediction, recurrence prediction, medical image processing, medical image registration, medical visual question answering, clinical report writing, medical image generation, and multimodal integration. This review aims to provide a comprehensive summary of the various AI methods utilized in lung cancer care, with a particular emphasis on machine learning and deep learning techniques. Moreover, with the advent and widespread application of large language models (LLMs), vision language models (VLMs), and multimodal integration for downstream clinical tasks, we explore the current landscape these cutting-edge AI tools offer. However, it also presents both significant challenges and opportunities, including data privacy risks, inherent biases that may exacerbate healthcare disparities, model hallucinations, ethical implications, implementation costs, and the lack of standardized evaluation metrics. Furthermore, the translation of these technologies from experimental research to clinical implementation demands comprehensive validation protocols and multidisciplinary collaboration to guarantee patient safety, therapeutic efficacy, and equitable healthcare delivery. This review emphasizes the critical role of AI in enhancing our understanding and management of lung cancer, ultimately striving for precision medicine and equitable healthcare worldwide.

Authors

  • Yi Luo
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Hamed Hooshangnejad
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Wilfred Ngwa
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA.
  • Kai Ding
    Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, College of Public Health.

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