DESIGN AND EVALUATION OF HYBRID EXPLAINABLE AI INTERFACES FOR VISION RESTORATION IN NEXT-GENERATION RETINAL PROSTHETICS

The development of retinal prosthetics has advanced significantly in recent years, yet challenges remain in achieving both high-quality vision restoration and interpretability of prosthetic function. This paper presents a novel framework—Bio-Optical Explainable Interfaces (BOEI)—which integrates biological signal modeling, optical encoding, and explainable artificial intelligence (XAI) to enhance both the efficacy and transparency of retinal prosthetic systems. BOEI employs a hybrid AI approach combining physics-informed neural networks with interpretable deep learning modules to translate visual stimuli into neural signals tailored for the damaged retina. The system models retinal ganglion cell responses while incorporating feedback loops that visualize and explain the decision-making processes of the AI components. Benchmarked against existing retinal interface models, BOEI demonstrates improved reconstruction accuracy (up to 27% over baseline models) and offers interpretable visual heatmaps correlating prosthetic output with retinal anatomy and cognitive perception metrics. The proposed framework represents a critical step toward clinically viable, trustworthy, and adaptive retinal prosthetics that align with both biological plausibility and patient-specific needs.