<scp>ViTCXRResNet</scp> : Harnessing Explainable Artificial Intelligence in Medical Imaging—Chest X‐Ray‐Based Patients Demographic Prediction

ABSTRACT Patient demographic prediction involves estimating age, gender, ethnicity, and other personal characteristics using X‐rays. This can help in personalized medicine and improved healthcare outcomes. It can assist in automated diagnosis for some diseases that exhibit age and gender‐specific prevalence. It can also help in forensic science to identify individuals when demographic information is missing. Insights from deep learning can verify the gender and age of self‐reported individuals through chest X‐rays (CXRs). In this proposed work, we have deployed an artificial intelligence (AI) enabled model which focuses on two tasks: gender classification and age prediction from CXRs. For gender classification, the model combines ResNet‐50 (CNN) and Vision Transformer (ViT) to leverage both local feature extraction and global contextual understanding for predicting gender and is called ViTCXRResNet. The model was trained and validated on an Amazon Web Services (SPR) dataset of 10702 images, split with an 80–20 ratio, which was evaluated with classification metrics to determine the model's behavior. For age prediction, extracted features from ResNet‐50 were used with dimensionality reduction through principal component analysis (PCA). A fully connected feedforward neural network was trained on the reduced feature set to predict age. The classification and regression model achieves accuracy results of 93.46% for gender classification and 0.86 for the R 2 score for age prediction on the SPR dataset. For visual interpretation, explainable AI (Gradient‐weighted Class Activation Mapping) was utilized to visualize and find out which parts of the image are prioritized for classifying gender. The proposed model yields high classification accuracy in gender detection and significant accuracy in age prediction. The model shows competitive accuracy compared to existing methods. Further, the demographic prediction stability of the model was proven on two different ethnic groups, such as the Japanese Society of Radiological Technology (JSRT) and Montgomery (USA) datasets.