AI-Driven Big Data Analytics for Precision Medicine and Healthcare Intelligence: A Unified Framework for Cancer, Chronic Disease, and Clinical Decision Optimization

Abstract

The explosion of various healthcare data from genomics to medical imaging to electronic health records, wearable sensors, and real-time clinical systems opens new opportunities for precision medicine. Yet traditional statistical and rule-based decision tools are stretched to the limits of analyzing the high-dimensional and multimodal and constantly changing data. Recent research has identified that AI-driven big data analytics can be used to scale up and provide better early detection of diseases, personalized treatment planning and clinical decision making in oncology and chronic diseases (Ahmed et al., 2025; Manik et al., 2025a). This paper synthesizes AI-enabled big data analytics for precision medicine, drawing on evidence from cancer diagnostics, prediction of chronic diseases, genomics-based drug discovery, and explainable clinical intelligence. It brings together the domains of deep learning, hybrid machine learning architectures, multimodal data fusion, and explainable AI (XAI) techniques that are subsumed into consolidated deep learning models into one unified framework that aims to enhance the performance of diagnostic accuracy, interpretability, and translational readiness (Forhad Hossain, 2025; Islam et al., 2025). The framework focuses on ethical data governance and scalable analytics pipelines, and clinician-centric transparency to bridge the gap between algorithmic innovation and real-world healthcare implementation. Synthesized results of findings show the consistent superiority of certain AI-relying big data analytics over traditional methods for disease classification, risk stratification, and therapeutic decision support and for certain other new challenges concerning model explainability, data privacy, and healthcare equity (Rahman et al., 2025; Samiun et al., 2025). By combining cross-domain evidence and proposing a unified analytical architecture, this work is designed to provide researchers, practitioners, and authorities with practical and useful knowledge by responding to the demand to operationalize at scale AI-powered precision medicine.

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