AI-Driven Ocular and Neurological Screening: A Comprehensive Review of the Swalife Eye Diagnostic Tool

Abstract

The convergence of artificial intelligence and ophthalmology has created unprecedented opportunities for early disease detection and neurological assessment through ocular biomarkers. This review examines the landscape of AI-based diagnostic systems with a particular focus on the Swalife Eye Diagnostic Tool, an integrated platform combining ocular disease detection with neurological indicator analysis. The tool addresses critical gaps in current screening methodologies by offering multi-disease capability, accessibility through standard image/video inputs, and automated clinical interpretation. We discuss the current state of AI in ocular diagnostics, the clinical rationale for integrated eye-brain assessment, the Swalife platform's architecture and functional capabilities, comparative advantages over existing systems, and clinical applications intelemedicine and population health. Strengths include rapid, scalable screening with standardized risk assessment and neurological metric evaluation; limitations include dependency on image quality and the need for comprehensive clinical validation. Future directions encompass expanded disease detection, integration with advanced imaging modalities, and real-time monitoring capabilities. The Swalife tool represents a significant advancement toward democratizing access to sophisticated ocular and neuro-oculomotor screening in diverse clinical and research settings.