Federated Artificial Intelligence and Cloud Computing Frameworks for Secure Healthcare Digital Transformation Strategies
DOI:
https://doi.org/10.15680/IJCTECE.2023.0606030Keywords:
Federated Learning, Artificial Intelligence, Cloud Computing, Healthcare Systems, Data Privacy, Secure Data Sharing, Medical Analytics, Digital Transformation, Cybersecurity, Distributed LearningAbstract
The healthcare industry is undergoing rapid digital transformation driven by the adoption of cloud computing, big data analytics, and Artificial Intelligence (AI). However, the sensitive nature of healthcare data raises critical concerns regarding privacy, security, and regulatory compliance. Federated Artificial Intelligence (Federated AI) has emerged as a promising approach to address these challenges by enabling collaborative model training without sharing raw data across institutions. This paper explores the integration of federated AI with cloud computing frameworks to support secure and scalable healthcare digital transformation strategies. By leveraging distributed learning, encryption techniques, and cloud-based infrastructure, federated AI ensures data privacy while enabling advanced analytics and decision-making. The study examines architectural models, implementation strategies, and real-world applications in healthcare systems, including disease prediction, medical imaging, and patient monitoring. It also highlights challenges such as data heterogeneity, communication overhead, and system scalability. The findings suggest that combining federated AI with cloud computing enhances data security, promotes interoperability, and supports compliance with healthcare regulations. This approach enables healthcare organizations to harness the power of AI while maintaining patient confidentiality, ultimately leading to improved clinical outcomes and more efficient healthcare delivery systems.
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