(untitled) (Eureka Journal of Health Sciences & Medical Innovation, 2026)
Early detection of diseases plays a pivotal role in improving patient outcomes, reducing healthcare costs, and enabling preventive medicine strategies. Traditional diagnostic procedures, however, often rely on invasive sampling, specialized laboratory equipment, and trained personnel, which limit timely access to accurate diagnostics, particularly in resource-constrained settings. The convergence of Artificial Intelligence (AI), hyperspectral imaging, and mobile computing provides a transformative approach to these challenges. This study presents the concept, architecture, and potential applications of S-SCAN, an AI-based hyperspectral smartphone system designed for non-invasive medical diagnostics. By leveraging machine learning algorithms, high-resolution hyperspectral imaging, and smartphone-based data acquisition, S-SCAN can detect subtle biochemical and physiological changes in human tissues that are otherwise invisible to conventional diagnostic methods. The system integrates deep learning-based spectral analysis, pattern recognition, and mobile decision support to enable rapid, accurate, and accessible diagnostics. The findings demonstrate that AI-driven hyperspectral diagnostics can significantly improve early disease detection, optimize patient care, and provide scalable solutions for digital health platforms.