Lung cancer remains a leading cause of mortality globally

with particularly high incidence rates in China. This review investigates the pivotal role of artificial intelligence (AI) in the clinical trials of lung cancer drugs

aiming to address the challenges of drug development and clinical research processes. The objective is to explore how AI technologies

including machine learning and deep learning

enhance clinical trials’ efficiency

accuracy

and personalization in this context. Our methodology involved a comprehensive literature search and analysis

focusing on integrating AI into various stages of clinical trials

namely

preclinical drug discovery

trial design

participant recruitment

and data analysis. The results indicate that AI demonstrates proficiency in integrating and analyzing extensive datasets

facilitating the identification of novel drug targets

and repurposing existing drugs. This capability enhances insights crucial for drug development

optimizes trial designs

streamlines participant recruitment

and conducts precise data analyses of trial outcomes. In conclusion

this review highlights the transformative potential of AI in lung cancer clinical trials and emphasizes the need for high-quality data collection and interpretability improvement to realize its benefits fully. Future research could focus on integrating multimodal approaches and multi-omics data into the entire process of clinical trials on lung cancer drugs to improve patient treatment outcomes and alleviate the global burden of lung cancer.