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This study introduces and confirms a novel architectural framework for integrating AI into intelligent commerce, specifically showing its transformative impact on the financial, retail, and supply chain sectors. Moving beyond a conceptual review, we propose the \\\"Data Nervous System\\\" framework and outline a quantitative method for its empirical evaluation. This framework positions AI as a dynamic \\\"data nervous system\\\" that ingests real-time signals, augments human decision-making, and drives strategic outcomes. We address the prevalent fear of AI as a replacement by presenting a nuanced perspective: AI’s primary role is augmentation, yielding significant business outcomes such as a 31% increase in retail sales conversions, a 200% improvement in fraud detection accuracy, and a 40% reduction in excess inventory. This research shows key challenges to AI adoption, including poor data quality, legacy infrastructure, and ethical concerns like bias and transparency. The findings support the conclusion that AI is a transformative enabler of adaptive systems in commerce, contingent upon strong data governance and a human-centered design approach to foster synergistic human-AI collaboration.

This study presents a critical analysis of Health Data Science and its evolving role in transforming healthcare delivery through advanced computational methodologies. By systematically integrating machine learning (ML), big data analytics, the Internet of Things (IoT), and extended reality (XR), the research demonstrates how these technologies contribute to early diagnosis, personalized treatment, and clinical decision support across diverse medical domains such as oncology, cardiology, diabetes care, radiology, and public health. The manuscript examines specific applications including predictive modeling for disease progression, federated learning for privacy-preserving data sharing, and multimodal image fusion using deep neural networks. It evaluates model performance using key metrics such as AUC-ROC and F1-score, highlighting both improvements over traditional diagnostic methods and current limitations in generalizability and real-world deployment. Ethical, legal, and data governance challenges are also discussed, with recommendations for enhancing transparency, fairness, and interoperability in health data systems. Through interdisciplinary collaboration and rigorous data practices, Health Data Science has the potential to foster more responsive, equitable, and patient-centered healthcare solutions. This work contributes actionable insights for clinicians, developers, and policymakers striving to leverage data-driven innovations in clinical environments