AI-Driven precision targeted therapy for EGFR-mutant non-small cell lung cancer: From therapeutic evolution and resistance mechanisms to intelligent decision systems

ObjectiveTo critically review the current pharmacologic treatment landscape for epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC), focusing on clinical pharmacology, therapeutic evolution, and emerging challenges of EGFR-targeted therapies.Data SourcesPeer-reviewed literature in PubMed, Web of Science, and Embase databases (January 2000 to June 2026) was searched using keywords including "EGFR-mutant NSCLC," "EGFR-TKIs," "drug resistance," "artificial intelligence," and "precision oncology." Reference lists and clinical trial registries were also screened.Data SummaryThis review systematically examines clinical misconceptions surrounding EGFR-targeted therapies, focusing on three cognitive biases that undermine precision implementation: overgeneralization of trial efficacy to unselected populations; oversimplified sequencing that ignores clonal selection trade-offs (second-generation TKIs reduce subsequent osimertinib Progression-free survival (PFS) by ∼2 months); and inflated efficacy perceptions for Ex20ins inhibitors (aggregate objective response rate (ORR) masks far-loop responses of only 22%) and MET combinations (true benefit confined to high-expression subgroup).