Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer

Immune checkpoint inhibitors (ICIs) have significantly improved the clinical outcomes for patients with non-small cell lung cancer (NSCLC). However, patient heterogeneity and the limitations of current biomarkers contribute to variations in therapeutic responses. Identifying potential beneficiaries of immunotherapy and predicting efficacy remain critical challenges. In recent years, artificial intelligence (AI) has become increasingly applied in cancer treatment, particularly for modeling clinical data and predicting patient prognosis. By integrating multi-omics data such as radiomics, pathomics, genomics, transcriptomics, proteomics, and microbiomics, AI enables comprehensive biomarker discovery and facilitates prediction of immunotherapy responses and potential toxicities in NSCLC patients. Despite these advancements, challenges such as data standardization, limited interpretability, and technical barriers persist. This review summarizes the application of AI in predicting immunotherapy efficacy for NSCLC patients and discusses the challenges and future directions in the context of precision medicine.