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TRV-2026-1017Certified recordPeer-reviewed

From Correlation to Clinical Translation: The Biological-Grounding×Translational-Readiness Framework for Artificial Intelligence in Non-Small-Cell Lung Cancer

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 retros…

Health · P Space — documented harm · certified 2026-09-08 · v1 · article view · machine-readable

Current reading — problem

In non-small-cell lung cancer, AI tools show strong benchmark performance but have repeatedly failed to translate into patient benefit because most models are retrospective, single-center, and validated only on metrics that do not track survival, toxicity, or procedural burden.

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Truvace Impact Record TRV-2026-1017, v1: “From Correlation to Clinical Translation: The Biological-Grounding×Translational-Readiness Framework for Artificial Intelligence in Non-Small-Cell Lung Cancer.” Truvace, 2026-09-08. /record/TRV-2026-1017 (accessed at citation time). sha256 a9ec7d0bbdc45d6c…

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