Chest Radiography AI Concordance and Lung Cancer Linkage in a Large Health Check-up Cohort

Purpose To evaluate the implementation characteristics of a commercially available chest radiography artificial intelligence (AI) system in a large real-world health check-up cohort using workflow-level, lesion-specific, and exploratory retrospective lung cancer case analyses. Methods This retrospective single-centre study included 298,991 consecutive health check-up chest radiographs from 114,866 individuals obtained between 2019 and 2023 and interpreted under routine double reading by board-certified radiologists. A commercially available AI system was evaluated using two prespecified thresholds: positivity in any of ten findings for the all-score analysis and positivity for nodule or mass for the nodule-focused analysis, both at a manufacturer-recommended score threshold of 15. Because routine radiologist judgement rather than universal CT or pathologic verification served as the reference framework, the primary analyses were interpreted as radiologist-referenced operational concordance analyses. Results Radiologist-referenced sensitivity and specificity were 72.0% and 79.6%, respectively, in the all-score analysis and 87.1% and 91.8%, respectively, in the nodule-focused analysis. Negative predictive values were 99.0% and 100.0%, respectively. Among 48 histopathologically confirmed lung cancer cases, retrospective timeline analyses showed earlier AI positivity than routine radiologist positivity in a subset of cases. These findings should be interpreted as exploratory observations and do not establish prospective clinical benefit. Conclusion In a large health check-up cohort, chest radiography AI demonstrated stable concordance with routine radiologist judgement and high sensitivity for radiologist-reported pulmonary nodules and masses. Exploratory retrospective analyses showed earlier AI positivity in a subset of histopathologically confirmed lung cancer cases, supporting further prospective evaluation of AI-assisted health check-up workflows.