From Correlation to Clinical Translation: The Biological-Grounding×Translational-Readiness Framework for Artificial...

Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer death worldwide, and clinicians now face a rapidly expanding array of artificial intelligence (AI) tools promising earlier detection, better treatment selection, and more precise radiotherapy, yet few have altered what happens at the bedside. The problem is not poor benchmark performance; it is that strong benchmark performance has repeatedly failed to translate into demonstrable patient benefit, because most published NSCLC models are retrospective, single-center, and validated only against metrics that do not track survival, toxicity, or procedural burden. This review argues that 2 orthogonal deficits explain that gap: an absence of biological grounding and an absence of lifecycle validation and introduces the biological-grounding×translational-readiness (BG×TR) matrix, an NSCLC-specific framework that locates any AI model along these 2 axes and identifies the single next study required to advance it toward clinical use. Applying this framework across the NSCLC care continuum, nodule detection, histopathologic and molecular inference, prognostic stratification, radiotherapy planning, immunotherapy response prediction, and disease surveillance, shows that the field's most biologically grounded models are rarely its most clinically validated, and vice versa. Spatial transcriptomics is proposed as a mechanistic ground-truth platform to close this gap. The review closes with a practical, clinician-facing agenda, biologically informed models, federated multi-institutional validation, and prospective adaptive trials, whose success should be measured not by AUROC but by longer, less toxic survival for patients with NSCLC.