Selective Diabetic Retinopathy Screening with Accuracy-Weighted Deep Ensembles and Entropy-Guided Abstention

AI can help address a major public health gap: The system achieved 93.70% accuracy in detecting diabetic retinopathy, a condition that currently goes undiagnosed in approximately 25% of cases worldwide and is projected to affect over 130 million people by 2030. This technology could help reduce preventable blindness by enabling screening in resource-limited settings where expert ophthalmologists are unavailable.

"Knowing what you don't know" is critical for medical AI: The system uses uncertainty quantification (entropy-guided abstention) to flag low-confidence predictions for human review rather than making unreliable automated decisions. This approach acknowledges that AI systems should not operate as black boxes in healthcare—they should communicate their confidence levels to enable safer clinical workflows.

Ensemble methods improve reliability over single AI models: Rather than relying on one neural network, the system combines seven different AI architectures (ResNet, DenseNet, MobileNet, and EfficientNet variants) and uses weighted voting to reach decisions. This redundancy approach mirrors how medical practice uses second opinions and reduces the risk of systematic errors from any single model.

Transparency and interpretability matter for clinical adoption: The paper emphasizes that limited interpretability and lack of uncertainty quantification have restricted clinical reliability of previous AI systems. By addressing these gaps, the framework aims to build trust with healthcare providers who need to understand why the AI makes recommendations, not just what it recommends.

Scalability could democratize screening access: The use of efficient architectures (like MobileNetV3) suggests the system could run on standard hardware in clinics worldwide, potentially reducing the cost and resource barriers that currently leave 25% of diabetic retinopathy cases undiagnosed.