AI and the World's Hard Problems · Medicine & drug discovery · Nature (computational biology) · 2026-10-17
A multi task deep learning framework for automated health condition prediction from retinal fundus images using hybrid attention and explainable AI
Existing deep learning models for retinal fundus images handle only one task, offer limited interpretability, and struggle with variation across datasets. The authors propose a multi-task network that classifies diabetic retinopathy severity, glaucoma, AMD stage, and cardiovascular risk from a single image in one forward pass, with Grad-CAM explanations. It reached 94.7% accuracy and AUC 0.978 across four datasets, outperforming all single-task models statistically, and AUC of at least 0.951 in zero-shot cross-dataset evaluation.