A Secure AI and Machine Learning–Enabled Cloud-Native Framework for Scalable Healthcare Analytics and API Interoperability

Fabio Giuseppe Serra

doi:10.15662/IJRAI.2024.0702008

Authors

Fabio Giuseppe Serra Senior Software Engineer, Italy Author

DOI:

https://doi.org/10.15662/IJRAI.2024.0702008

Keywords:

Healthcare Analytics, Cloud-Native Architecture, Artificial Intelligence, Machine Learning, Secure Software Engineering, API Interoperability, MLOps, Microservices, Healthcare Data Security, Scalable Systems

Abstract

The rapid adoption of digital health platforms has led to an exponential growth in healthcare data, creating the need for secure, scalable, and intelligent analytics systems that can operate across heterogeneous applications and services. This paper proposes a secure AI and machine learning–enabled cloud-native framework designed for scalable healthcare analytics and seamless API interoperability. The framework integrates cloud-native software engineering principles with advanced machine learning pipelines to support real-time and batch analytics over structured and unstructured healthcare data. Security and privacy are embedded by design through encrypted data exchange, role-based and zero-trust access control, secure API gateways, and compliance-aware data handling aligned with healthcare regulations. Machine learning models are deployed as containerized microservices and managed using automated MLOps pipelines, enabling continuous model training, validation, and deployment at scale. Standardized APIs facilitate interoperability between electronic health records, clinical decision support systems, and third-party healthcare services. Experimental evaluation demonstrates improved analytics scalability, reduced data processing latency, and enhanced interoperability compared to traditional monolithic healthcare systems. The proposed framework provides a practical reference architecture for building next-generation healthcare analytics platforms that are secure, interoperable, and capable of supporting AI-driven clinical and operational intelligence.

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