Artificial Intelligence and the Future of NSCLC

Artificial intelligence (AI) has the potential to transform biomarker testing in early-stage NSCLC by streamlining data analysis, improving interpretation of complex genomic profiles, and predicting actionable mutations from imaging or pathology data. Despite advances, significant unmet needs remain in early-stage NSCLC diagnosis, including limited screening uptake, challenges in obtaining sufficient tissue for comprehensive testing, and delayed identification of high-risk patients. In management, addressing these gaps through personalized treatment pathways, enhanced multidisciplinary coordination, and predictive analytics could improve outcomes and reduce recurrence in early-stage NSCLC.