Lung Cancer

Lung cancer research intensely focuses on improving early detection and prognosis prediction to reduce mortality rates. Current efforts utilize advanced machine learning models, including convolutional neural networks, vision transformers, and large language models, to analyze multi-modal data such as CT scans, PET scans, genomic information, and patient records, often employing techniques like multi-modal fusion and attention mechanisms to enhance accuracy. These advancements aim to improve diagnostic accuracy, personalize treatment plans, and refine survival predictions, ultimately leading to better patient outcomes and informing clinical decision-making. The integration of these AI-driven approaches with traditional methods holds significant promise for transforming lung cancer care.