Abstract

This review paper examines the role of artificial intelligence (AI) in the early detection of age-related diseases, highlighting its potential to transform healthcare through advanced health data analytics. With the aging population worldwide, the prevalence of age-related diseases such as Alzheimer's, diabetes, and cardiovascular conditions continues to rise, underscoring the need for innovative diagnostic approaches. The paper explores key AI technologies, including machine learning, deep learning, and natural language processing, and their applications in analyzing diverse health data sources such as electronic health records, wearable devices, genomic data, and medical imaging. Despite the significant benefits of improved diagnostic accuracy and predictive capabilities, the review also addresses critical challenges, including data privacy, algorithmic bias, and integration into clinical practice. Finally, the paper offers recommendations for policymakers, researchers, and healthcare providers to facilitate the effective implementation of AI-driven early detection systems, ultimately enhancing patient outcomes and promoting health equity.