Abstract
Anemia, a global health concern affecting over 2 billion people, is traditionally diagnosed through invasive and resource-intensive blood tests. These methods can be a hassle, expensive, and sometimes impossible to access, especially in low resource settings. This review paper synthesizes key research on how Artificial Intelligence (AI) and Deep Learning (DL) are transforming diagnostics by developing non-invasive, accessible, and scalable alternatives. Instead of drawing blood, AI models are being trained to analyze subtle physiological changes. The research in this domain primarily focuses on leveraging visual and physiological data that can be captured easily, often with a smartphone.
