Diagnostic Model

Diagnostic models leverage machine learning to improve the accuracy and efficiency of disease detection, focusing on challenges like early diagnosis, generalizability across diverse datasets, and interpretability of model predictions. Current research employs various architectures, including deep learning (e.g., ResNet, 3D U-Net, transformers), ensemble methods, and generative models to analyze multimodal data (imaging, genomics, clinical records) and enhance model robustness against data biases. These advancements hold significant potential for improving healthcare by enabling earlier interventions, reducing diagnostic errors, and optimizing resource allocation, particularly in resource-constrained settings.