A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning

TL;DR

This review highlights how large AI models are revolutionizing lung cancer screening, diagnosis, and treatment by improving accuracy, enabling multimodal data integration, and supporting personalized medicine, while also discussing current challenges and future directions.

Contribution

It systematically categorizes and evaluates recent large AI models applied to lung cancer, emphasizing their architectures, performance, and clinical potential, which is a novel comprehensive synthesis.

Findings

Large AI models improve lung nodule detection accuracy.

Multimodal AI models enable better prognosis and treatment planning.

Emerging clinical deployment shows promising validation results.

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.