Building Intelligent Systems from Data Silos Enabling Trustworthy AI and Cloud Ecosystems for Modern Healthcare
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Abstract
Modern healthcare systems generate vast amounts of heterogeneous data across hospitals, laboratories, wearable devices, and electronic health records. However, these data remain fragmented in silos, limiting their potential to support intelligent decision-making. This paper explores the development of intelligent systems that integrate siloed healthcare data to enable trustworthy artificial intelligence (AI) and cloud-driven ecosystems. It highlights the challenges associated with data fragmentation, interoperability, privacy, and security while emphasizing the importance of federated architectures and standardized data exchange protocols.
The study proposes a framework that combines cloud computing, AI models, and privacy-preserving techniques such as federated learning and differential privacy to facilitate secure data sharing without compromising patient confidentiality. By leveraging scalable cloud infrastructure, healthcare providers can achieve real-time analytics, predictive modeling, and personalized treatment recommendations.
Furthermore, the research underscores the role of trustworthiness in AI systems, including transparency, fairness, accountability, and robustness. The integration of explainable AI mechanisms ensures that clinical decisions are interpretable and reliable. The findings suggest that overcoming data silos can significantly enhance healthcare delivery, improve patient outcomes, and foster innovation. Ultimately, the study advocates for a collaborative, secure, and interoperable healthcare ecosystem powered by trustworthy AI and cloud technologies
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