A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning
Journal:
arXiv
Published Date:
Jun 8, 2025
Abstract
Lung cancer remains one of the most prevalent and fatal diseases worldwide,
demanding accurate and timely diagnosis and treatment. Recent advancements in
large AI models have significantly enhanced medical image understanding and
clinical decision-making. This review systematically surveys the
state-of-the-art in applying large AI models to lung cancer screening,
diagnosis, prognosis, and treatment. We categorize existing models into
modality-specific encoders, encoder-decoder frameworks, and joint encoder
architectures, highlighting key examples such as CLIP, BLIP, Flamingo,
BioViL-T, and GLoRIA. We further examine their performance in multimodal
learning tasks using benchmark datasets like LIDC-IDRI, NLST, and MIMIC-CXR.
Applications span pulmonary nodule detection, gene mutation prediction,
multi-omics integration, and personalized treatment planning, with emerging
evidence of clinical deployment and validation. Finally, we discuss current
limitations in generalizability, interpretability, and regulatory compliance,
proposing future directions for building scalable, explainable, and clinically
integrated AI systems. Our review underscores the transformative potential of
large AI models to personalize and optimize lung cancer care.
