AI-Driven Big Data Analytics for Precision Medicine: A Unified Framework Integrating Molecular Data Intelligence, Wearable Health Systems, and Predictive Modeling

Abstract

The rapid digitization of healthcare and the massive growth of high-dimensional biomedical data have shed light on fundamental limitations regarding conventional medical decision-making, which is population-based. Precision medicine aims to overcome such limitations by incorporating biological, clinical, and behavioral data on individual levels with the purpose of achieving personalized diagnosis, treatment, and prevention. Artificial intelligence (AI) and big data analytics are the key enablers in the operationalization of precision medicine, including enabling a scalable interrogation of heterogeneous data sources - including multi-omics profiles and electronic health records, medical imaging and wearable sensor data. This investigation provides the overarching, data-centric synthesis of AI-based big data analytics in precision medicine, bringing into the spotlight integrated analytical architectures on the basis of predictive, preventive, and personalized delivery of healthcare. Drawing on a structured analytical evaluation methodology, the investigation draws together evidence from higher levels of healthcare AI scholarship to assess the efficacy of multiple data modalities integration, predictive modelling, and governance-aware systems design. The results have shown that the integrated AI frameworks greatly improve disease risk stratification, real-time health monitoring and clinical decision management compared to siloed analytic methodology. The research adds a coherent conceptual framework addressing the topics of scalability, interpretability, privacy, and ethical governance in order to provide reachable information to practice and implement trustworthy AI systems concordant with global objectives for healthcare and sustainability.

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