This review highlights the rapidly evolving role of artificial intelligence (AI) in transforming lung cancer care, with a specific focus on its integrated applications across diagnosis, biomarker discovery, and drug development. The novelty of this work lies in its holistic examination of how AI bridges these traditionally separate domains, from radiology and pathology to genomics and clinical trials, to create a more cohesive and personalized oncology pipeline. We detail how AI algorithms significantly enhance early detection by improving the accuracy and efficiency of pulmonary nodule characterization on computed tomography scans and enable precise cancer subtyping via computational pathology. In biomarker discovery, AI-driven analysis of radiomic features and genomic data facilitates the non-invasive prediction of tumor genotype, PD-L1 expression, and immunotherapy response, moving beyond invasive tissue biopsies. Furthermore, AI is accelerating the drug development lifecycle by identifying novel therapeutic targets and optimizing patient selection for clinical trials. The review also explores AI’s critical role in personalizing treatment regimens, including predicting outcomes for radiotherapy and immunotherapy, thereby tailoring therapy to individual patient profiles. We critically address the challenges of clinical translation, including model interpretability, data standardization, and ethical considerations, which are pivotal for real-world implementation. Finally, we contend that the future of lung cancer management hinges on robust, multi-institutional validation of AI tools and the development of trustworthy, explainable systems.
Basety et al. (2026) studied this question.