ArticleNPJ digital medicine2026
Advancing diagnostic equity through artificial intelligence chest radiograph screening for osteoporosis in Asian populations.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Early identification of abnormal bone mineral density (BMD) through opportunistic screening is critical for preventing osteoporotic fractures. We validated an AI model in 2384 asymptomatic adults (57.7% female; mean age 43.6 years) 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%). Although BMI distributions paralleled East Asian regional trends, intersectional subgroup analyses remain exploratory due to small event counts. Decision curve analysis indicated superior net benefit for AI-based referral over "refer all" or "refer none" strategies, particularly for women with normal BMI (18.5-23 kg/m²). This AI tool offers precise triage for Asian health examination populations, though further validation in multi-center cohorts is required to confirm broad generalizability.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.