Revolutionizing Disease Diagnosis and Prediction with AI in Biomedical Data

Abstract

The integration of artificial intelligence (AI) into biomedical data analysis has transformed disease diagnosis and prediction, offering unprecedented accuracy, scalability, and cost-efficiency. This paper explores cutting-edge AI techniques—including deep learning, multimodal data fusion, and federated learning—applied to imaging, genomic, and clinical data. We present a rigorous analysis of AI-driven frameworks, benchmarking their performance against traditional diagnostic tools. Key advancements such as convolutional neural networks (CNNs) in radiology, natural language processing (NLP) for genomic literature mining, and predictive models for chronic diseases are critically evaluated. Technical challenges, ethical considerations, and future directions (e.g., quantum AI, edge computing) are discussed to outline a roadmap for clinical adoption. Supported by empirical data and comparative tables, this study underscores AI’s potential to reduce diagnostic errors by up to 40% and enable early disease detection with 92% AUC-ROC scores.