Technical Acquisition Parameters Dominate Demographic Factors in Chest X-ray AI Performance Disparities: A Multi-Dataset External Validation Study

Artificial intelligence systems for chest radiograph interpretation are increasingly deployed in clinical practice, yet current fairness frameworks emphasize demographic subgroup analysis while the relative contribution of technical acquisition parameters to performance disparities remains poorly characterized. We conducted a multi-dataset external validation study analyzing 138,804 chest radiographs from the RSNA Pneumonia Detection Challenge (n=26,684; 22.5% pneumonia prevalence) and NIH ChestX-ray14 (n=112,120; 1.3% prevalence) using five pre-trained DenseNet-121 models. We calculated sensitivity, specificity, and area under the receiver operating characteristic curve stratified by view type (anteroposterior versus posteroanterior), age group, and sex, with variance decomposition quantifying each factor's contribution to performance variation. View type dominated performance variance in both datasets: 87% in RSNA and 69% in NIH. All five models demonstrated systematic posteroanterior view underdiagnosis with miss rates of 30-78%. The odds ratio for missed diagnosis on posteroanterior versus anteroposterior views was 6.69 (95% CI: 5.79-7.72) in RSNA and 13.02 (95% CI: 11.62-14.59) in NIH. Analysis of 131,361 disease-free images demonstrated that view-type effects persist strongly even without disease (Cohen's d = 1.19-1.33), definitively refuting the hypothesis that observed disparities reflect disease severity confounding rather than learned image characteristics. Age explained 5-30% of variance depending on dataset, while sex consistently explained less than 2%. Technical acquisition parameters, specifically radiograph view type, dominate performance disparities in chest X-ray AI substantially exceeding demographic factor contributions. These findings have immediate implications for regulatory frameworks: future FDA and EU AI Act guidance should explicitly mandate acquisition parameter auditing alongside demographic subgroup analysis.