Multimodal Deep Learning for Pulmonary Nodule Detection on Chest Radiography in High‐Risk Adults, With Secondary Validation for All‐Cause and Cause‐Specific Mortality Prediction: A Multicenter Cohort Study

= 24,697); its fused embeddings were used as covariates in Cox proportional-hazards models, and time-dependent AUCs were evaluated at 1-12 years. For nodule detection, AUCs were 0.979 (0.975-0.982) in TLCID and 0.849 (0.835-0.862) in ChestDR; the TLCID stratified model reached 0.990 (0.984-0.994). In PLCO, AUCs were 0.925 (0.892-0.952) for all-cause mortality and 0.939-0.985 for cardiac-, lung cancer-, and Chronic Obstructive Pulmonary Disease (COPD)-cause mortality, with robust subgroup performance. These results support CXR-based nodule flagging within screening workflows and suggest secondary opportunistic risk stratification potential.