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Foundation Model Robustness to Technical Acquisition Parameters in Chest X-Ray AI A Multi-Architecture Comparative Study with External Validation
2026-01-27
radiology and imaging
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BackgroundFoundation models have emerged as a promising paradigm for medical imaging AI [7], with claims of improved generalization and reduced bias. However, their robustness to technical acquisition parameters remains unexplored. We evaluated whether foundation models exhibit greater robustness to chest radiograph view type (anteroposterior [AP] versus posteroanterior [PA]) compared to traditional convolutional neural networks. MethodsWe compared four model architectures on the RSNA Pneumonia...
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