Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
This work addresses diagnostic accuracy and fairness in clinical AI for chest radiology, though it is incremental as it builds on existing multimodal approaches.
The authors tackled the problem of limited diagnostic accuracy and fairness in chest radiology deep learning models by introducing MetaCheX, a multimodal framework that integrates chest X-ray images with patient metadata, resulting in improved overall diagnostic accuracy measured by an increase in AUROC on the CheXpert Plus dataset.
Existing deep learning models for chest radiology often neglect patient metadata, limiting diagnostic accuracy and fairness. To bridge this gap, we introduce MetaCheX, a novel multimodal framework that integrates chest X-ray images with structured patient metadata to replicate clinical decision-making. Our approach combines a convolutional neural network (CNN) backbone with metadata processed by a multilayer perceptron through a shared classifier. Evaluated on the CheXpert Plus dataset, MetaCheX consistently outperformed radiograph-only baseline models across multiple CNN architectures. By integrating metadata, the overall diagnostic accuracy was significantly improved, measured by an increase in AUROC. The results of this study demonstrate that metadata reduces algorithmic bias and enhances model generalizability across diverse patient populations. MetaCheX advances clinical artificial intelligence toward robust, context-aware radiographic disease detection.