Iridology as a Predictive Tool for Human Health: Innovating Non-Invasive Diagnostics

Iridology, the study of the iris to reveal systemic health, has faced challenges related to data privacy, clinical validity, and diagnostic limitations. This paper introduces a novel AI-driven framework that integrates federated learning (FL), convolutional neural networks (CNNs), genetic algorithms (GA), and edge computing to enhance iridology’s reliability and practicality. The proposed system preserves data privacy while improving predictive accuracy through decentralized learning and genomic correlation analysis. Experimental results demonstrate strong clinical performance, achieving a diabetes prediction accuracy of 90% and cardiovascular risk prediction of 86% [1], with real-time deployment on NVIDIA Jetson Nano ensuring an average inference time of 45ms per image [2]. Furthermore, genetic feature selection identified iris biomarkers linked to diseaseassociated genes, improving diagnostic precision [3]. Clinical validation across diverse demographics confirmed robustness, with no significant variation across age groups (p = 0.23), genders (p = 0.45), or ethnicities (p = 0.12) [4]. This hybrid approach represents a significant advancement in non-invasive diagnostics, bridging AI-driven iris analysis with genetic predisposition insights. Future work will focus on integrating multi-modal imaging techniques and expanding federated learning across global datasets to enhance scalability and applicability in clinical settings [5].