This study aimed to examine the feasibility and performance of AI-enabled chest radiograph (AI-CXR) screening within the context of routine preventive care.
Specifically, we sought to evaluate the capability of an AI-CXR tool to flag suspected abnormal bone mineral density (saBMD), with the goal of identifying individuals who meet clinical criteria for further diagnostic confirmation.
We validated an AI model in 2,384 asymptomatic adults undergoing health examinations in Taiwan.
Using DXA as the reference, the model identified 255 suspected abnormal BMD cases, with 94 (3.9%) DXA-confirmed positive.
Population-level performance was robust, yielding an AUC of 0.95 (95% CI 0.93–0.99) and sensitivity of 79.7% (95% CI 71.3–86.5%).
This AI tool offers precise triage for Asian health examination populations, though further validation in multi-center cohorts is required to confirm broad generalizability.
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