Clinical Artificial Intelligence in Non-Small Cell Lung Cancer: From Early Detection and Computational Pathology to Precision Oncology

Artificial intelligence (AI) is being developed to analyze radiological, histopathological, molecular, and clinical data across the non-small cell lung cancer (NSCLC) pathway. This narrative review examines representative applications through August 2026, focusing on intended clinical use, validation, and the evidence required to influence care. Studies were selected purposively for clinical relevance and methodological importance. Imaging models support investigation of nodule detection, malignancy estimation, and future lung cancer risk, although predictive performance does not establish the benefit of changing screening or diagnostic pathways. In computational pathology, models classify tissue, quantify morphological features, and estimate selected molecular alterations. Foundation models extend these capabilities, but their evaluation requires attention to pretraining provenance and independence of downstream testing. Histology-based molecular prediction does not currently justify replacing validated molecular assays; prioritization of testing requires prospective workflow evaluation. Models evaluated in immunotherapy-treated cohorts can predict response or survival, but these associations do not establish differential benefit between treatments. The principal limitations include selected retrospective populations, incomplete assessment of calibration and clinically relevant thresholds, uncertain transportability, and limited evaluation of model-guided care. Evidence should therefore be interpreted at the level of the individual system and intended decision. Prospective evaluation, appropriate comparators, explicit failure procedures, and post-deployment monitoring are required before technical performance can be translated into clinical benefit.