Abstract

Early disease detection plays a crucial role in improving patient outcomes and reducing healthcare costs. Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) have enabled automated analysis of facial biomarkers for identifying potential health conditions. Facial biomarkers such as skin discoloration, facial asymmetry, eye abnormalities, and texture variations may indicate underlying diseases. Although deep learning models have demonstrated remarkable performance in medical image analysis, their black-box nature limits clinical trust and adoption. Explainable Artificial Intelligence (XAI) techniques provide transparency by explaining model predictions and highlighting important facial features associated with disease detection. This review explores the integration of explainable deep learning with facial biomarker analysis for early disease detection. The paper discusses facial biomarkers, deep learning architectures, explainability techniques, applications, challenges, and future research directions.