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Big Data in Medicine

Big Data in Medicine involves collecting, analyzing, and interpreting extremely large and complex healthcare datasets to improve clinical decision-making and patient outcomes. These datasets include electronic health records, imaging files, genomic data, wearable sensor outputs, and population health statistics. By applying AI and advanced analytics, big data helps identify disease patterns, predict risks, and personalize treatment strategies. It supports public health surveillance, clinical research, and operational efficiency in hospitals. Big data also enables early detection of outbreaks and optimizes resource allocation. Overall, it empowers healthcare systems to become more proactive, precise, and evidence-driven.

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    • Journal Highlights

    Artificial Intelligence in Healthcare Machine Learning and Deep Learning Medical Imaging and Diagnostics Predictive Analytics Digital Health Technologies Telemedicine and mHealth Wearable Devices and Remote Monitoring Health Informatics and EHR Big Data in Medicine Robotics and Automation AI in Drug Discovery Healthcare Cybersecurity Smart Hospital Systems Clinical Decision Support Systems Natural Language Processing

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