Artificial intelligence in clinical trials of lung cancer: Current and future prospects

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.