Cloud security architectures for AI-enabled healthcare diagnostics and personalized treatment plans

Akinniyi James Samuel *

Akin James LLC, Technology Director, Houston, Texas, United State.
 
Review
World Journal of Advanced Engineering Technology and Sciences, 2024, 11(01), 467-484.
Article DOI: 10.30574/wjaets.2024.11.1.0036
Publication history: 
Received on 17 December 2023; revised on 26 January 2024; accepted on 29 January 2024
 
Abstract: 
The integration of artificial intelligence (AI) into healthcare diagnostics and personalized treatment planning has introduced unprecedented opportunities for precision medicine, yet it concurrently escalates the complexity of ensuring secure, compliant, and resilient computing infrastructures. This research explores the design and deployment of cloud security architectures tailored to the sensitive operational context of AI-enabled healthcare ecosystems. Emphasis is placed on the security frameworks and protocols necessary to safeguard patient data confidentiality, ensure integrity and availability of diagnostic algorithms, and maintain regulatory compliance under frameworks such as HIPAA and GDPR. The study evaluates architectural models including multi-cloud and hybrid-cloud deployments, examining their implications for access control, federated identity management, secure data storage, and real-time threat detection. Additionally, it investigates cryptographic techniques, secure multi-party computation, and homomorphic encryption in the context of distributed AI workloads. The paper concludes by identifying current limitations, proposing optimized security mechanisms, and outlining directions for future research in building robust, scalable, and privacy-preserving cloud infrastructures for AI-driven healthcare solutions.
 
Keywords: 
Cloud Security; Artificial Intelligence; Healthcare Diagnostics; Personalized Treatment; Data Privacy; Federated Learning; Cryptographic Protocols; HIPAA Compliance; Threat Detection; Secure Architecture
 
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Copyright information: 
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0