Foundation Model Robustness to Technical Acquisition Parameters in Chest X-Ray AI A Multi-Architecture Comparative Study with External Validation
This study demonstrates that while foundation models for chest X-ray AI show varying degrees of robustness to acquisition parameters like view type, their performance advantages are often dataset-specific and fail to generalize across external validation, revealing that technical biases persist regardless of model architecture or training scale.