RetinaVision: XAI-Driven Augmented Regulation for Precise Retinal Disease Classification using deep learning framework

High accuracy achieved with interpretability: The study demonstrated that deep learning models (Xception at 95.25% accuracy) can effectively classify retinal diseases from OCT images while incorporating explainability tools like GradCAM and LIME—addressing a key responsible AI concern about "black box" medical decisions that clinicians need to understand and trust.

Data augmentation improves reliability: The use of advanced augmentation techniques (CutMix and MixUp) enhanced model generalization, which is important for responsible AI because it helps ensure the system performs reliably across diverse patient populations and real-world conditions, not just on training data.

Practical deployment with safeguards: The authors implemented their findings in a real-world web application (RetinaVision), demonstrating that responsible AI principles—combining accuracy with interpretability—can be operationalized in actual clinical settings rather than remaining theoretical.

Interpretability as a clinical requirement: The explicit emphasis on implementing "both accuracy and interpretability for clinical applications" reflects responsible AI best practices, recognizing that clinicians need to understand why the system makes recommendations to maintain accountability and catch potential errors.

Large, diverse dataset foundation: Training on 24,000 labeled images across eight different retinal conditions provides a more robust foundation than smaller datasets, supporting responsible AI principles around data quality and representativeness for medical applications.